Search results for: patent sentiment analysis

Authors: Rohan Erasala, Ian McCulloh

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The NFL Draft is a chance for every NFL team to select their next superstar. As a result, teams heavily invest in scouting, and millions of fans partake in the online discourse surrounding the draft. This paper investigates the potential correlations between positive sentiment in individual draft selection threads from the subreddit r/NFL and if this data can be used to make successful player recommendations. It is hypothesized that there will be limited correlations and nonviable recommendations made from these threads. The hypothesis is tested using sentiment analysis of draft thread comments and analyzing correlation and precision at k of top scores. The results indicate weak correlations between the percentage of positive comments in a draft selection thread and a player’s approximate value, but potentially viable recommendations from looking at players whose draft selection threads have the highest percentage of positive comments.

Keywords: national football league, NFL, NFL Draft, sentiment analysis, Reddit, social media, NLP

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Authors: Sergio Andres Rojas, Julian Benavides Franco, Juan Tomas Sayago

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An increasing amount of research demonstrates how market mood affects financial markets, but their primary goal is to demonstrate how Trump's tweets impacted US interest rate volatility. Following that lead, this work evaluates the effect that Trump's tweets had during his presidency on local and international stock markets, considering not just volatility but the direction of the movement. Three indexes for Trump's tweets were created relating his activity with movements in the S&P500 using natural language analysis and machine learning algorithms. The indexes consider Trump's tweet activity and the positive or negative market sentiment they might inspire. The first explores the relationship between tweets generating negative movements in the S&P500; the second explores positive movements, while the third explores the difference between up and down movements. A pseudo-investment strategy using the indexes produced statistically significant above-average abnormal returns. The findings also showed that the pseudo strategy generated a higher return in the local market if applied to intraday data. However, only a negative market sentiment caused this effect on daily data. These results suggest that the market reacted primarily to a negative idea reflected in the negative index. In the international market, it is not possible to identify a pervasive effect. A rolling window regression model was also performed. The result shows that the impact on the local and international markets is heterogeneous, time-changing, and differentiated for the market sentiment. However, the negative sentiment was more prone to have a significant correlation most of the time.

Keywords: market sentiment, Twitter market sentiment, machine learning, natural dialect analysis

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Authors: Marc Solé, Francesc Giné, Magda Valls, Nina Bijedic

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What people say on social media has turned into a rich source of information to understand social behavior. Specifically, the growing use of Twitter social media for political communication has arisen high opportunities to know the opinion of large numbers of politically active individuals in real time and predict the global political tendencies of a specific country. It has led to an increasing body of research on this topic. The majority of these studies have been focused on polarized political contexts characterized by only two alternatives. Unlike them, this paper tackles the challenge of forecasting Spanish political trends, characterized by multiple political parties, by means of analyzing the Twitters Users political tendency. According to this, a new strategy, named Tweets Analysis Strategy (TAS), is proposed. This is based on analyzing the users tweets by means of discovering its sentiment (positive, negative or neutral) and classifying them according to the political party they support. From this individual political tendency, the global political prediction for each political party is calculated. In order to do this, two different strategies for analyzing the sentiment analysis are proposed: one is based on Positive and Negative words Matching (PNM) and the second one is based on a Neural Networks Strategy (NNS). The complete TAS strategy has been performed in a Big-Data environment. The experimental results presented in this paper reveal that NNS strategy performs much better than PNM strategy to analyze the tweet sentiment. In addition, this research analyzes the viability of the TAS strategy to obtain the global trend in a political context make up by multiple parties with an error lower than 23%.

Keywords: political tendency, prediction, sentiment analysis, Twitter

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Authors: Andrea Schiffauerova, Amnah Alzeyoudi

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This paper studies patterns of innovation within national constitutional context. Its objective is to examine national systems of innovation in biotechnology in six leading innovative countries: the US, Japan, Germany, the UK, France and Canada. The framework proposed for this purpose consists of specific factors considered critical for the development of national systems of innovation, which are industry size, innovative activities, area of specialization, industry structure, national policy, the level of government intervention, the stock of knowledge in universities and industries, knowledge transfer from universities to industry and country-specific conditions for start-ups. The paper then uses the framework to provide detailed cross-country comparisons while highlighting particular features of national institutional context which affect the creation and diffusion of scientific knowledge within the system. The study is primarily based on the extensive survey of literature and it is complemented by the quantitative analysis of the patent data extracted from the United States Patent and Trademark Office (USPTO). The empirical analysis provides numerous insights and greatly complements the data gained from the literature and other sources. The final cross-country comparative analysis identifies three patterns followed by the national innovation systems in the six countries. The proposed cross-country relative positioning analysis may help in drawing policy implications and strategies leading to the enhancement of national competitive advantage and innovation capabilities of nations.

Keywords: comparative analysis, framework, national systems of innovation, patent analysis, United States Patent and Trademark Office (USPTO)

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Authors: Yoonjung An, Yongtae Park

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Knowledge flows are a critical source of faster technological progress and stouter economic growth. Knowledge flows have been accelerated dramatically with the establishment of a patent system in which each patent is required by law to disclose sufficient technical information for the invention to be recreated. Patent analysis, thus, has been widely used to help investigate technological knowledge flows. However, the existing research is limited in terms of both subject and approach. Particularly, in most of the previous studies, business method (BM) patents were not covered although they are important drivers of knowledge flows as other patents. In addition, these studies usually focus on the static analysis of knowledge flows. Some use approaches that incorporate the time dimension, yet they still fail to trace a true dynamic process of knowledge flows. Therefore, we investigate dynamic patterns of knowledge flows driven by BM patents using a Hidden Markov Model (HMM). An HMM is a popular statistical tool for modeling a wide range of time series data, with no general theoretical limit in regard to statistical pattern classification. Accordingly, it enables characterizing knowledge patterns that may differ by patent, sector, country and so on. We run the model in sets of backward citations and forward citations to compare the patterns of knowledge utilization and knowledge dissemination.

Keywords: business method patents, dynamic pattern, Hidden-Markov Model, knowledge flow

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Authors: Alissa Lefebre

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It might seem intuitive to hope for a fast decision on the patent grant. After all, a granted patent provides you with a monopoly position, which allows you to obstruct others from using your technology. However, this does not take into account the strategic advantages one can obtain from keeping their patent applications pending. First, you have the financial advantage of postponing certain fees, although many applicants would probably agree that this is not the main benefit. As the scope of the patent protection is only decided upon at the grant, the pendency period introduces uncertainty amongst rivals. This uncertainty entails not knowing whether the patent will actually get granted and what the scope of protection will be. Consequently, rivals can only depend upon limited and uncertain information when deciding what technology is worth pursuing. One way to keep patent applications pending, is the use of divisional applications. These applicants can be filed out of a parent application as long as that parent application is still pending. This allows the applicant to pursue (part of) the content of the parent application in another application, as the divisional application cannot exceed the scope of the parent application. In a fast-moving and complex market such as the tele- and digital communications, it might allow applicants to obtain an actual monopoly position as competitors are discouraged to pursue a certain technology. Nevertheless, this practice also has downsides to it. First of all, it has an impact on the workload of the examiners at the patent office. As the number of patent filings have been increasing over the last decades, using strategies that increase this number even more, is not desirable from the patent examiners point of view. Secondly, a pending patent does not provide you with the protection of a granted patent, thus not only create uncertainty for the rivals, but also for the applicant. Consequently, the European patent office (EPO) has come up with a “raising the bar initiative” in which they have decided to tackle the strategic use of divisional applications. Over the past years, two rules have been implemented. The first rule in 2010 introduced a time limit, upon which divisional applications could only be filed within a 24-month limit after the first communication with the patent office. However, after carrying-out a user feedback survey, the EPO abolished the rule again in 2014 and replaced it by a fee mechanism. The fee mechanism is still in place today, which might be an indication of a better result compared to the first rule change. This study tests the impact of these rules on the strategic use of divisional applications in the tele- and digital communication industry and provides empirical evidence on their success. Upon using three different survival models, we find overall evidence that divisional applications prolong the pendency time and that only the second rule is able to tackle the strategic patenting and thus decrease the pendency time.

Keywords: divisional applications, regulatory changes, strategic patenting, EPO

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Authors: Eugene C. Lim

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This paper seeks to examine the extent to which ‘forum shopping’ is available to patent filers seeking protection of GMO (genetically modified organisms)-based inventions in Hong Kong. Under Hong Kong’s current re-registration system for standard patents, an inventor must first seek patent protection from one of three Designated Patent Offices (DPO) – those of the People’s Republic of China (PRC), the Europe Union (EU) (designating the UK), or the United Kingdom (UK). The ‘designated patent’ can then be re-registered by the successful patentee in Hong Kong. Interestingly, however, the EU and the PRC do not adopt a harmonized approach toward the patenting of GMOs, and there are discrepancies in their interpretation of the phrase ‘animal or plant variety’. In view of these divergences, the ability to effectively manage ‘conflict of law’ issues is an important priority for multinational biotechnology firms with a patent portfolio in the Greater China region. Generally speaking, both the EU and the PRC exclude ‘animal and plant varieties’ from the scope of patentable subject matter. However, in the EU, Article 4(2) of the Biotechnology Directive allows a genetically modified plant or animal to be patented if its ‘technical feasibility is not limited to a specific variety’. This principle has allowed for certain ‘transgenic’ mammals, such as the ‘Harvard Oncomouse’, to be the subject of a successful patent grant in the EU. There is no corresponding provision on ‘technical feasibility’ in the patent legislation of the PRC. Although the PRC has a sui generis system for protecting plant varieties, its patent legislation allows the patenting of non-biological methods for producing transgenic organisms, not the ‘organisms’ themselves. This might lead to a situation where an inventor can obtain patent protection in Hong Kong over transgenic life forms through the re-registration of a patent from a more ‘biotech-friendly’ DPO, even though the subject matter in question might not be patentable per se in the PRC. Through a comparative doctrinal analysis of legislative provisions, cases and court interpretations, this paper argues that differences in the protection afforded to GMOs do not generally prejudice the ability of global MNCs to obtain patent protection in Hong Kong. Corporations which are able to first obtain patents for GMO-based inventions in Europe can generally use their European patent as the basis for re-registration in Hong Kong, even if such protection might not be available in the PRC itself. However, the more restrictive approach to GMO-based patents adopted in the PRC would be more acutely felt by enterprises and inventors based in mainland China. The broader scope of protection offered to GMO-based patents in Europe might not be available in Hong Kong to mainland Chinese patentees under the current re-registration model for standard patents, unless they have the resources to apply for patent protection as well from another (European) DPO as the basis for re-registration.

Keywords: biotechnology, forum shopping, genetically modified organisms (GMOs), greater China region, patent portfolio

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Authors: Jaehyung An, Sungjoo Lee

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Patent documents constitute an up-to-date and reliable source of knowledge for reflecting technological advance, so patent analysis has been widely used for identification of technological trends and formulation of technology strategies. But, identifying technological information from patent data entails some limitations such as, high cost, complexity, and inconsistency because it rely on the expert’ knowledge. To overcome these limitations, researchers have applied to a quantitative analysis based on the keyword technique. By using this method, you can include a technological implication, particularly patent documents, or extract a keyword that indicates the important contents. However, it only uses the simple-counting method by keyword frequency, so it cannot take into account the sematic relationship with the keywords and sematic information such as, how the technologies are used in their technology area and how the technologies affect the other technologies. To automatically analyze unstructured technological information in patents to extract the semantic information, it should be transformed into an abstracted form that includes the technological key concepts. Specific sentence structure ‘SAO’ (subject, action, object) is newly emerged by representing ‘key concepts’ and can be extracted by NLP (Natural language processor). An SAO structure can be organized in a problem-solution format if the action-object (AO) states that the problem and subject (S) form the solution. In this paper, we propose the new methodology that can extract the SAO structure through technical elements extracting rules. Although sentence structures in the patents text have a unique format, prior studies have depended on general NLP (Natural language processor) applied to the common documents such as newspaper, research paper, and twitter mentions, so it cannot take into account the specific sentence structure types of the patent documents. To overcome this limitation, we identified a unique form of the patent sentences and defined the SAO structures in the patents text data. There are four types of technical elements that consist of technology adoption purpose, application area, tool for technology, and technical components. These four types of sentence structures from patents have their own specific word structure by location or sequence of the part of speech at each sentence. Finally, we developed algorithms for extracting SAOs and this result offer insight for the technology innovation process by providing different perspectives of technology.

Keywords: NLP, patent analysis, SAO, semantic-analysis

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Authors: Samaneh Nadali, Masrah Azrifah Azmi Murad

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As a result of the growth of the web, people are able to express their views and opinions. They can now post reviews of products at merchant sites and express their views on almost anything in internet forums, discussion groups, and blogs. Therefore, the number of product reviews has grown rapidly. The large numbers of reviews make it difficult for manufacturers or businesses to automatically classify them into different semantic orientations (positive, negative, and neutral). For sentiment classification, most existing methods utilize a list of opinion words whereas this paper proposes a fuzzy approach for evaluating sentiments expressed in customer product reviews, to predict the strength levels (e.g. very weak, weak, moderate, strong and very strong) of customer product reviews by combinations of adjective, adverb and verb. The proposed fuzzy approach has been tested on eight benchmark datasets and obtained 74% accuracy, which leads to help the organization with a more clear understanding of customer's behavior in support of business planning process.

Keywords: fuzzy logic, customer product review, sentiment analysis

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Authors: Sarah O. Alhumoud, Mawaheb I. Altuwaijri, Tarfa M. Albuhairi, Wejdan M. Alohaideb

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Large-scale data stream analysis has become one of the important business and research priorities lately. Social networks like Twitter and other micro-blogging platforms hold an enormous amount of data that is large in volume, velocity and variety. Extracting valuable information and trends out of these data would aid in a better understanding and decision-making. Multiple analysis techniques are deployed for English content. Moreover, one of the languages that produce a large amount of data over social networks and is least analyzed is the Arabic language. The proposed paper is a survey on the research efforts to analyze the Arabic content in Twitter focusing on the tools and methods used to extract the sentiments for the Arabic content on Twitter.

Keywords: big data, social networks, sentiment analysis, twitter

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Authors: Alev Atak

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In this paper, we aim to create a financial sentiment index by investigating the company’s voluntary information disclosures. We retrieve structured content from BIST 100 companies’ financial reports for the period 1998-2018 and extract relevant financial information for sentiment analysis through Natural Language Processing. We measure strategy-related disclosures and their cross-sectional variation and classify report content into generic sections using synonym lists divided into four main categories according to their liquidity risk profile, risk positions, intra-annual information, and exposure to risk. We use Word Error Rate and Cosin Similarity for comparing and measuring text similarity and derivation in sets of texts. In addition to performing text extraction, we will provide a range of text analysis options, such as the readability metrics, word counts using pre-determined lists (e.g., forward-looking, uncertainty, tone, etc.), and comparison with reference corpus (word, parts of speech and semantic level). Therefore, we create an adequate analytical tool and a financial dictionary to depict the importance of granular financial disclosure for investors to identify correctly the risk-taking behavior and hence make the aggregated effects traceable.

Keywords: financial sentiment, machine learning, information disclosure, risk

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Authors: Aghamusa Azizov

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This study investigates the correlation between solar activity and sentiment in news media coverage, using a large-scale dataset of solar activity since 1750 and over 15 million articles from "The New York Times" dating from 1851 onwards. Employing Pearson's correlation coefficient and multiple Natural Language Processing (NLP) tools—TextBlob, Vader, and DistillBERT—the research examines the extent to which fluctuations in solar phenomena are reflected in the sentiment of historical news narratives. The findings reveal that the correlation between solar activity and media sentiment is generally negligible, suggesting a weak influence of solar patterns on the portrayal of events in news media. Notably, a moderate positive correlation was observed between the sentiments derived from TextBlob and Vader, indicating consistency across NLP tools. The analysis provides insights into the historical impact of solar activity on human affairs and highlights the importance of using multiple analytical methods to understand complex relationships in large datasets. The study contributes to the broader understanding of how extraterrestrial factors may intersect with media-reported events and underlines the intricate nature of interdisciplinary research in the data science and historical domains.

Keywords: solar activity correlation, media sentiment analysis, natural language processing, historical event patterns

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Authors: Valery Yakubovich, Shuping wu

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Organization theorists have long claimed that organizational innovations are nontechnological, in part because they are unpatentable. The claim rests on the assumption that organizational innovations are abstract ideas embodied in persons and contexts rather than in context-free practical tools. However, over the last three decades, organizational knowledge has been increasingly embodied in digital tools which, in principle, can be patented. To provide the first empirical evidence regarding the patentability of organizational innovations, we trained two machine learning algorithms to identify a population of 205,434 patent applications for organizational technologies (OrgTech) and, among them, 141,285 applications that use organizational innovations accumulated over the 20th century. Our event history analysis of the probability of patenting an OrgTech invention shows that ideas from organizational innovations decrease the probability of patent allowance unless they describe a practical tool. We conclude that the present-day digital transformation places organizational innovations in the realm of high tech and turns the debate about organizational technologies into the challenge of designing practical organizational tools that embody big ideas about organizing. We outline an agenda for patent-based research on OrgTech as an emerging phenomenon.

Keywords: organizational innovation, organizational technology, high tech, patents, machine learning

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Authors: Kseniia Zviagintseva, Brett Buttliere

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Science is fundamentally a problem-solving enterprise, and scientists pay more attention to the negative things, that cause them dissonance and negative affective state of uncertainty or contradiction. While this is agreed upon by philosophers of science, there are few empirical demonstrations. Here we examine the keywords from those papers published by PLoS in 2014 and show with several sentiment analyzers that negative keywords are studied more than positive keywords. Our dataset is the 927,406 keywords of 32,870 scientific articles in all fields published in 2014 by the journal PLOS ONE (collected from Altmetric.com). Counting how often the 47,415 unique keywords are used, we can examine whether those negative topics are studied more than positive. In order to find the sentiment of the keywords, we utilized two sentiment analysis tools, Hu and Liu (2004) and SentiStrength (2014). The results below are for Hu and Liu as these are the less convincing results. The average keyword was utilized 19.56 times, with half of the keywords being utilized only 1 time and the maximum number of uses being 18,589 times. The keywords identified as negative were utilized 37.39 times, on average, with the positive keywords being utilized 14.72 times and the neutral keywords - 19.29, on average. This difference is only marginally significant, with an F value of 2.82, with a p of .05, but one must keep in mind that more than half of the keywords are utilized only 1 time, artificially increasing the variance and driving the effect size down. To examine more closely, we looked at those top 25 most utilized keywords that have a sentiment. Among the top 25, there are only two positive words, ‘care’ and ‘dynamics’, in position numbers 5 and 13 respectively, with all the rest being identified as negative. ‘Diseases’ is the most studied keyword with 8,790 uses, with ‘cancer’ and ‘infectious’ being the second and fourth most utilized sentiment-laden keywords. The sentiment analysis is not perfect though, as the words ‘diseases’ and ‘disease’ are split by taking 1st and 3rd positions. Combining them, they remain as the most common sentiment-laden keyword, being utilized 13,236 times. More than just splitting the words, the sentiment analyzer logs ‘regression’ and ‘rat’ as negative, and these should probably be considered false positives. Despite these potential problems, the effect is apparent, as even the positive keywords like ‘care’ could or should be considered negative, since this word is most commonly utilized as a part of ‘health care’, ‘critical care’ or ‘quality of care’ and generally associated with how to improve it. All in all, the results suggest that negative concepts are studied more, also providing support for the notion that science is most generally a problem-solving enterprise. The results also provide evidence that negativity and contradiction are related to greater productivity and positive outcomes.

Keywords: bibliometrics, keywords analysis, negativity bias, positive and negative words, scientific papers, scientometrics

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Authors: Emiel Caron

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Patent databases contain patent related data, organized in a relational data model, and are used to produce various patent statistics. These databases store raw data about scientific references cited by patents. For example, Patstat holds references to tens of millions of scientific journal publications and conference proceedings. These references might be used to connect patent databases with bibliographic databases, e.g. to study to the relation between science, technology, and innovation in various domains. Problematic in such studies is the low data quality of the references, i.e. they are often ambiguous, unstructured, and incomplete. Moreover, a complete bibliographic reference is stored in only one attribute. Therefore, a computerized cleaning and disambiguation method for large patent databases is developed in this work. The method uses rule-based scoring and clustering. The rules are based on bibliographic metadata, retrieved from the raw data by regular expressions, and are transparent and adaptable. The rules in combination with string similarity measures are used to detect pairs of records that are potential duplicates. Due to the scoring, different rules can be combined, to join scientific references, i.e. the rules reinforce each other. The scores are based on expert knowledge and initial method evaluation. After the scoring, pairs of scientific references that are above a certain threshold, are clustered by means of single-linkage clustering algorithm to form connected components. The method is designed to disambiguate all the scientific references in the Patstat database. The performance evaluation of the clustering method, on a large golden set with highly cited papers, shows on average a 99% precision and a 95% recall. The method is therefore accurate but careful, i.e. it weighs precision over recall. Consequently, separate clusters of high precision are sometimes formed, when there is not enough evidence for connecting scientific references, e.g. in the case of missing year and journal information for a reference. The clusters produced by the method can be used to directly link the Patstat database with bibliographic databases as the Web of Science or Scopus.

Keywords: clustering, data cleaning, data disambiguation, data mining, patent analysis, scientometrics

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Authors: M. G. Cattaneo

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The non-effectiveness of certain codified human rights is particularly apparent with reference to the lack of access to essential drugs in developing countries, which represents a breach of the human right to receive adequate health assistance. This paper underlines the conflict and the legal contradictions between human rights, namely health rights, international Intellectual Property Rights, in particular patent law, as well as international trade law. The paper discusses the crucial links between R&D costs for innovation, patents and new medical drugs, with the goal of reformulating the hierarchies of priorities and of interests at stake in the international intellectual property (IP) law system. Different from what happens today, International patent law should be a legal instrument apt at rebalancing an axiological asymmetry between the (conflicting) needs at stake The core argument in the paper is the proposal of an alternative pathway, namely a feasible proposal for a patent law reform. IP laws tend to balance the benefits deriving from innovation with the costs of the provided monopoly, but since developing countries and industrialized countries are in completely different political and economic situations, it is necessary to (re)modulate such exchange according to the different needs. Based on this critical analysis, the paper puts forward a proposal, called Trading Time for Space (TTS), whereby a longer time for patent exclusive life in western countries (Time) is offered to the patent holder company, in exchange for the latter selling the medical drug at cost price in developing countries (Space). Accordingly, pharmaceutical companies should sell drugs in developing countries at the cost price, or alternatively grant a free license for the sale in such countries, without any royalties or fees. However, such social service shall be duly compensated. Therefore, the consideration for such a service shall be an extension of the temporal duration of the patent’s exclusive in the country of origin that will compensate the reduced profits caused by the supply at the price cost in developing countries.

Keywords: global health, global justice, patent law reform, access to drugs

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Authors: A. M. John-Otumu, M. M. Rahman, O. C. Nwokonkwo, M. C. Onuoha

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Online social media networks have long served as a primary arena for group conversations, gossip, text-based information sharing and distribution. The use of natural language processing techniques for text classification and unbiased decision-making has not been far-fetched. Proper classification of this textual information in a given context has also been very difficult. As a result, we decided to conduct a systematic review of previous literature on sentiment classification and AI-based techniques that have been used in order to gain a better understanding of the process of designing and developing a robust and more accurate sentiment classifier that can correctly classify social media textual information of a given context between hate speech and inverted compliments with a high level of accuracy by assessing different artificial intelligence techniques. We evaluated over 250 articles from digital sources like ScienceDirect, ACM, Google Scholar, and IEEE Xplore and whittled down the number of research to 31. Findings revealed that Deep learning approaches such as CNN, RNN, BERT, and LSTM outperformed various machine learning techniques in terms of performance accuracy. A large dataset is also necessary for developing a robust sentiment classifier and can be obtained from places like Twitter, movie reviews, Kaggle, SST, and SemEval Task4. Hybrid Deep Learning techniques like CNN+LSTM, CNN+GRU, CNN+BERT outperformed single Deep Learning techniques and machine learning techniques. Python programming language outperformed Java programming language in terms of sentiment analyzer development due to its simplicity and AI-based library functionalities. Based on some of the important findings from this study, we made a recommendation for future research.

Keywords: artificial intelligence, natural language processing, sentiment analysis, social network, text

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Authors: Tayyaba Azim, Bibi Amina

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The increase in the popularity of opinion mining with the rapid growth in the availability of social networks has attracted a lot of opportunities for research in the various domains of Sentiment Analysis and Natural Language Processing (NLP) using Artificial Intelligence approaches. The latest trend allows the public to actively use the internet for analyzing an individual’s opinion and explore the effectiveness of published facts. The main theme of this research is to account the public opinion on the most crucial and extensively discussed development projects, China Pakistan Economic Corridor (CPEC), considered as a game changer due to its promise of bringing economic prosperity to the region. So far, to the best of our knowledge, the theme of CPEC has not been analyzed for sentiment determination through the ML approach. This research aims to demonstrate the use of ML approaches to spontaneously analyze the public sentiment on Twitter tweets particularly about CPEC. Support Vector Machine SVM is used for classification task classifying tweets into positive, negative and neutral classes. Word2vec and TF-IDF features are used with the SVM model, a comparison of the trained model on manually labelled tweets and automatically generated lexicon is performed. The contributions of this work are: Development of a sentiment analysis system for public tweets on CPEC subject, construction of an automatic generation of the lexicon of public tweets on CPEC, different themes are identified among tweets and sentiments are assigned to each theme. It is worth noting that the applications of web mining that empower e-democracy by improving political transparency and public participation in decision making via social media have not been explored and practised in Pakistan region on CPEC yet.

Keywords: machine learning, natural language processing, sentiment analysis, support vector machine, Word2vec

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Authors: José Carlos Rodríguez, Mario Gómez

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This paper analyses innovation trends in Latin America countries by means of the number of patent applications filed by residents and non-residents during the period 1965 to 2012. Making use of patent data released by the World Intellectual Property Organization (WIPO), we search for the presence of multiple structural changes in patent application series in Argentina, Brazil Chile, and Mexico. These changes may suggest that firms’ innovative activity has been modified as a result of implementing a particular science, technology and innovation (STI) policy. Accordingly, the new regulations implemented in these countries during 1980s and 1990s have influenced their intellectual property regimes. The question conducting this research is thus how STI policies in these countries have affected their innovation activity? The results achieved in this research confirm the existence of multiple structural changes in the series of patent applications resulting from STI policies implemented in these countries.

Keywords: econometric methods, innovation activity, Latin America countries, patents, science, technology and innovation policy

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Authors: Christina Wainikka, Besrat Tesfaye

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Several of the countries in the Baltic Sea region are ranked high in international innovations rankings, such as the Global Innovation Index and European Innovation Scoreboard. There are however some concerns in the performance of different countries. For example, there is a widely spread notion about “The Swedish Paradox”. Sweden is ranked high due to investments in R&D and patent activity, but the outcome is not as high as could be expected. SMEs in Sweden are also below EU average when it comes to registering intellectual property rights such as patents and trademarks. This study is concentrating on the protection of utility model. This intellectual property right does not exist in Sweden, but in for example Finland and Germany. The utility model protection is sometimes referred to as a “patent light” since it is easier to obtain than the patent protection but at the same time does cover technical solutions. In examining statistics on patent activities and activities in registering utility models it is clear that utility model protection is scarcely used in the countries that have the protection. In Germany 10 577 applications were made in 2021. In Finland there were 259 applications made in 2021. This can be compared with patent applications that were 58 568 in Germany in 2021 and 1 662 in Finland in 2021. In Sweden there has never been a protection for utility models. The only protection for technical solutions is patents and business secrets. The threshold for obtaining a patent is high, due to the legal requirements and the costs. The patent protection is there for often not chosen by SMEs in Sweden. This study examines whether the protection of utility models in other countries in the Baltic region provide SMEs in these countries with better options to protect their innovations. The legal methodology is comparative law. In order to study the effects of the legal differences statistics are examined and interviews done with SMEs from different industries.

Keywords: baltic sea region, comparative law, SME, utility model

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Authors: Sandhya Baskaran, Hari Kumar Nagabushanam

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Sarcasm sentiment recognition is an area of natural language processing that is being probed into in the recent times. Even with the advancements in NLP, typical translations of words, sentences in its context fail to provide the exact information on a sentiment or emotion of a user. For example, if something bad happens, the statement ‘That's just what I need, great! Terrific!’ is expressed in a sarcastic tone which could be misread as a positive sign by any text-based analyzer. In this paper, we are presenting a unique real time ‘word with its tone’ spotting technique which would provide the sentiment analysis for a tone or pitch of a voice in combination with the words being expressed. This hybrid approach increases the probability for identification of special sentiment like sarcasm much closer to the real world than by mining text or speech individually. The system uses a tone analyzer such as YIN-FFT which extracts pitch segment-wise that would be used in parallel with a speech recognition system. The clustered data is classified for sentiments and sarcasm score for each of it determined. Our Simulations demonstrates the improvement in f-measure of around 12% compared to existing detection techniques with increased precision and recall.

Keywords: sarcasm recognition, tone-word spotting, natural language processing, pitch analyzer

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Authors: Kevin Smith

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Publicly traded company in the US are legally obligated to host earnings calls that discuss their most recent financial reports. During these calls, investors are able to ask these companies questions about these financial reports and on the future direction of the company. This paper examines whether common institutional owners use these calls as a way to indirectly signal to companies in their portfolio to not take actions that could hurt the common owner's interests. This paper uses transcripts taken from the earnings calls of the six largest health insurance companies in the US from 2014 to 2019. This data is analyzed using text analysis and sentiment analysis to look for patterns in the statements made by common owners. The analysis found that common owners where more likely to recommend against direct price competition and instead redirect the insurance companies towards more passive actions, like investing in new technologies. This result indicates a mechanism that common owners use to reduce competition in the health insurance market.

Keywords: common ownership, text analysis, sentiment analysis, machine learning

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Authors: Nikola Radovanovic, Petros Gkotsis, Mathieu Doussineau

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Aeronautics is regarded as a strategically important sector for European competitiveness. It was at the heart of European entrepreneurial development since the industry was born. Currently, the EU is the world leader in the production of civil aircraft, including helicopters, aircraft engines, parts, and components. It is recording a surplus in trade relating to aerospace products, which are exported all over the globe. Also, this industry shows above-average investments in research and development, as demonstrated in the patent activity in this area. The post-pandemic recovery of the industry will partly depend on the possibilities to streamline collaboration in further research and innovation activities. Aeronautics features as one of the often selected priority domains in smart specialisation, which represents the main regional and national approach in developing and implementing innovation policies in Europe. The basis for the selection of priority domains for smart specialisation lies in the mapping of innovative potential, with research and patent activities being among the key elements of this analysis. This research is aimed at identifying characteristics of the trends in research and patent activities in the regions and countries that base their competitiveness on the aeronautics sector. It is also aimed at determining the scope and patterns of collaborations in aeronautics between innovators from the European regions, focusing on revealing new technology areas that emerge from these collaborations. For this purpose, we developed a methodology based on desk research and the analysis of the PATSTAT patent database as well as the databases of R&I framework programmes.

Keywords: aeronautics, smart specialisation, innovation, research, regional policy

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Authors: Aya Salama

Abstract:

Digital Twin is an emerging research topic that attracted researchers in the last decade. It is used in many fields, such as smart manufacturing and smart healthcare because it saves time and money. It is usually related to other technologies such as Data Mining, Artificial Intelligence, and Machine Learning. However, Human digital twin (HDT), in specific, is still a novel idea that still needs to prove its feasibility. HDT expands the idea of Digital Twin to human beings, which are living beings and different from the inanimate physical entities. The goal of this research was to create a Human digital twin that is responsible for real-time human replies automation by simulating human behavior. For this reason, clustering, supervised classification, topic extraction, and sentiment analysis were studied in this paper. The feasibility of the HDT for personal replies generation on social messaging applications was proved in this work. The overall accuracy of the proposed approach in this paper was 63% which is a very promising result that can open the way for researchers to expand the idea of HDT. This was achieved by using Random Forest for clustering the question data base and matching new questions. K-nearest neighbor was also applied for sentiment analysis.

Keywords: human digital twin, sentiment analysis, topic extraction, supervised machine learning, unsupervised machine learning, classification, clustering

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Authors: Tarek Sadraoui, Ahlem Nasr Othman

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Our research deal with studying and testing the effects of social media on the cryptocurrency price during the period 2020-2023. The rise of the phenomena of cryptocurrency in the world raises questions about the importance of sentiment analysis of social media on the price of the cryptocurrency. Using panel data, we show that the positive and negative twits have a positive and statistically significant impact on the price of the cryptocurrency, and neutral twits have exerted a negative and significant effect on the cryptocurrency price. Specifically, we determine the causal relationship, short-term and long-term relationship with ARDL approach between the cryptocurrency price and social media using the Granger causality test.

Keywords: social media, Twitter, Google trend, panel, cryptocurrency

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Authors: Xiangjun Kong, Dongning Yao, Yuanjia Hu

Abstract:

Therapeutic antibodies have become the most valuable and successful class of biopharmaceutical drugs, with a huge market potential and therapeutic advantages. Antibody patents are, accordingly, extremely important. As the technological limitation of the early stage of this field, the U. S. Patent and Trademark Offices (USPTO) have issued guidelines that suggest an exception for patents claiming a genus of antibodies that bind to a novel antigen, even in the absence of any experimental antibody production. This 'antibody exception' allowed for a broad scope on antibody claims, and led a global trend to patent antibodies without antibodies. Disputes around the pertinent patentability and written description issues remain particularly intense. Yet the validity of such patents had not been overtly challenged until Centocor v. Abbott, which restricted the broad scope of antibody patents and hit the brakes on the 'antibody exception'. The courts tend to uphold the requirement for adequate description of antibodies in the patent specifications, to avoid overreaching antibody claims. Patents following the 'antibody exception' are at risk of being found invalid for inadequately describing what they have claimed. However, the relation between the court and USPTO guidelines remains obscure, and the waning of the 'antibody exception' has led to further disputes around antibody patents. This uncertainty clearly affects patent applications, antibody innovations, and even relevant business performance. This study will give an overview of the emergence, debate, and waning usage of the 'antibody exception' in a number of enlightening cases, attempting to understand the specific concerns and the potential influence of antibody patents. We will then provide some possible strategies for antibody patenting, under the current considerations on the 'antibody exception'.

Keywords: antibody exception, antibody patent, USPTO (U. S. Patent and Trademark Offices) guidelines, written description requirement

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Authors: Chih-Hsiang Chang

Abstract:

This study provides insight into the effects of investor sentiment, excess optimism, overconfidence, the disposition effect, and herding formation on momentum profits. This study contributes to the field by providing a further examination of the relationship between psychological factors and momentum profits. The empirical results show that there is no evidence of significant momentum profits in Taiwan’s stock market. Additionally, investor sentiment in Taiwan’s stock market significantly influences its momentum profits.

Keywords: momentum profits, psychological factors, herding formation, investor sentiment

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Authors: Mohsen Farhadloo, Majid Farhadloo

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Aspect-level opinion mining that aims at discovering aspects (aspect identification) and their corresponding ratings (sentiment identification) from customer reviews have increasingly attracted attention of researchers and practitioners as it provides valuable insights about products/services from customer's points of view. Instead of addressing aspect identification and sentiment identification in two separate steps, it is possible to simultaneously identify both aspects and sentiments. In recent years many graphical models based on Latent Dirichlet Allocation (LDA) have been proposed to solve both aspect and sentiment identifications in a single step. Although LDA models have been effective tools for the statistical analysis of document collections, they also have shortcomings in addressing some unique characteristics of opinion mining. Our goal in this paper is to address one of the limitations of topic models to date; that is, they fail to directly model the associations among topics. Indeed in many text corpora, it is natural to expect that subsets of the latent topics have higher probabilities. We propose a probabilistic graphical model called focus-LDA, to better capture the associations among topics when applied to aspect-level opinion mining. Our experiments on real-life data sets demonstrate the improved effectiveness of the focus-LDA model in terms of the accuracy of the predictive distributions over held out documents. Furthermore, we demonstrate qualitatively that the focus-LDA topic model provides a natural way of visualizing and exploring unstructured collection of textual data.

Keywords: aspect-level opinion mining, document modeling, Latent Dirichlet Allocation, LDA, sentiment analysis

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Authors: Ariadna Gassiot, Raquel Camprubi, Lluis Coromina

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Creative tourism involves the participation of tourists in the co-creation of their own experiences in a tourism destination. Consequently, creative tourists move from a passive behavior to an active behavior, and tourism destinations address this type of tourism by changing the scenario and making tourists learn and participate while they travel instead of merely offering tourism products and services to them. In creative tourism experiences, tourists are in close contact with locals and their culture. In destinations where culture (i.e. food, heritage, etc.) is the basis of their offer, such as Girona, Spain, tourism stakeholders must especially consider, analyze, and further foster the co-creation of authentic tourism experiences. They should focus on discovering more about these experiences, their main attributes, visitors’ opinions, etc. Creative tourists do not only participate while they travel around the world, but they also have and active post-travel behavior. They feel free to write about tourism experiences in different channels. User-generated content becomes crucial for any tourism destination when analyzing the market, making decisions, planning strategies, and when addressing issues, such as their reputation and performance. Sentiment analysis is a methodology used to automatically analyze semantic relationships and meanings in texts, so it is a way to extract tourists’ emotions and feelings. Tourists normally express their views and opinions regarding tourism products and services. They may express positive, neutral or negative feelings towards these products or services. For example, they may express anger, love, hate, sadness or joy towards tourism services and products. They may also express feelings through verbs, nouns, adverbs, adjectives, among others. Sentiment analysis may help tourism professionals in a range of areas, from marketing to customer service. For example, sentiment analysis allows tourism stakeholders to forecast tourism expenditure and tourist arrivals, or to analyze tourists’ profile. While there is an increasing presence of creativity in tourists’ experiences, there is also an increasing need to explore tourists’ expressions about these experiences. There is a need to know how they feel about participating in specific tourism activities. Thus, the main objective of this study is to analyze the meanings, emotions and feelings that tourists express about their creative experiences in Girona, Spain. To do so, sentiment analysis methodology is used. Results show the diversity of tourists who actively participate in tourism in Girona. Their opinions refer both to tangible aspects (e.g. food, museums, etc.) and to intangible aspects (e.g. friendliness, nightlife, etc.) of tourism experiences. Tourists express love, likeliness and other sentiments towards tourism products and services in Girona. This study can help tourism stakeholders in understanding tourists’ experiences and feelings. Consequently, they can offer more customized products and services and they can efficiently make them participate in the co-creation of their own tourism experiences.

Keywords: creative tourism, sentiment analysis, text mining, user-generated content

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Authors: Stefan Papastefanou

Abstract:

Artificial intelligence (AI) is an interdisciplinary field of computer science with the aim of creating intelligent machine behavior. Early approaches to AI have been configured to operate in very constrained environments where the behavior of the AI system was previously determined by formal rules. Knowledge was presented as a set of rules that allowed the AI system to determine the results for specific problems; as a structure of if-else rules that could be traversed to find a solution to a particular problem or question. However, such rule-based systems typically have not been able to generalize beyond the knowledge provided. All over the world and especially in IT-heavy industries such as the United States, the European Union, Singapore, and China, machine learning has developed to be an immense asset, and its applications are becoming more and more significant. It has to be examined how such products of machine learning models can and should be protected by IP law and for the purpose of this paper patent law specifically, since it is the IP law regime closest to technical inventions and computing methods in technical applications. Genetic breeding models are currently less popular than recursive neural network method and deep learning, but this approach can be more easily described by referring to the evolution of natural organisms, and with increasing computational power; the genetic breeding method as a subset of the evolutionary algorithms models is expected to be regaining popularity. The research method focuses on patentability (according to the world’s most significant patent law regimes such as China, Singapore, the European Union, and the United States) of AI inventions and machine learning. Questions of the technical nature of the problem to be solved, the inventive step as such, and the question of the state of the art and the associated obviousness of the solution arise in the current patenting processes. Most importantly, and the key focus of this paper is the problem of patenting inventions that themselves are developed through machine learning. The inventor of a patent application must be a natural person or a group of persons according to the current legal situation in most patent law regimes. In order to be considered an 'inventor', a person must actually have developed part of the inventive concept. The mere application of machine learning or an AI algorithm to a particular problem should not be construed as the algorithm that contributes to a part of the inventive concept. However, when machine learning or the AI algorithm has contributed to a part of the inventive concept, there is currently a lack of clarity regarding the ownership of artificially created inventions. Since not only all European patent law regimes but also the Chinese and Singaporean patent law approaches include identical terms, this paper ultimately offers a comparative analysis of the most relevant patent law regimes.

Keywords: algorithms, inventor, genetic breeding models, machine learning, patentability

Procedia PDF Downloads 91