Search results for: Opinion spam

Authors: Mohammadali Tavakoli, Atefeh Heydari, Zuriati Ismail, Naomie Salim

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With the increasing number of people reviewing products online in recent years, opinion sharing websites has become the most important source of customers’ opinions. Unfortunately, spammers generate and post fake reviews in order to promote or demote brands and mislead potential customers. These are notably destructive not only for potential customers, but also for business holders and manufacturers. However, research in this area is not adequate, and many critical problems related to spam detection have not been solved to date. To provide green researchers in the domain with a great aid, in this paper, we have attempted to create a highquality framework to make a clear vision on review spam-detection methods. In addition, this report contains a comprehensive collection of detection metrics used in proposed spam-detection approaches. These metrics are extremely applicable for developing novel detection methods.

Keywords: Fake reviews, Feature collection, Opinion spam, Spam detection.

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Authors: Claudiu N.Musat

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Due to the constant increase in the volume of information available to applications in fields varying from medical diagnosis to web search engines, accurate support of similarity becomes an important task. This is also the case of spam filtering techniques where the similarities between the known and incoming messages are the fundaments of making the spam/not spam decision. We present a novel approach to filtering based solely on layout, whose goal is not only to correctly identify spam, but also warn about major emerging threats. We propose a mathematical formulation of the email message layout and based on it we elaborate an algorithm to separate different types of emails and find the new, numerically relevant spam types.

Keywords: Clustering, layout, k-means, spam.

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Authors: Yanti Rosmunie Bujang, Husnayati Hussin

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This paper attempts to discuss the spam issue from the Malaysian e-mail users- perspective. The purpose is to discover how Malaysian users handle the spam e-mail problem. From the experiences we hope to discover the necessary effort needed to be undertaken to face this problem in the context of Malaysia. A survey was conducted to understand how Malaysian individual perceived spam and what they actually do with the spam e-mail they received in their daily life. The findings indicate that the level of awareness on spam issue in action is still low and need some extra effort by government and relevant agencies to increase their level of awareness.

Keywords: E-mail, Malaysia, spam, users' perspective.

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Authors: Fahim A., Mutahira N. Naseem

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Spam is any unwanted electronic message or material in any form posted too many people. As the world is growing as global world, social networking sites play an important role in making world global providing people from different parts of the world a platform to meet and express their views. Among different social networking sites Facebook become the leading one. With increase in usage different users start abusive use of Facebook by posting or creating ways to post spam. This paper highlights the potential spam types nowadays Facebook users’ faces. This paper also provide the reason how user become victim to spam attack. A methodology is proposed in the end discusses how to handle different types of spam.

Keywords: Artificial neural networks, Facebook spam, social networking sites, spam filter.

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Authors: T. Kumaresan, S. Sanjushree, C. Palanisamy

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Image spam is a kind of email spam where the spam text is embedded with an image. It is a new spamming technique being used by spammers to send their messages to bulk of internet users. Spam email has become a big problem in the lives of internet users, causing time consumption and economic losses. The main objective of this paper is to detect the image spam by using histogram properties of an image. Though there are many techniques to automatically detect and avoid this problem, spammers employing new tricks to bypass those techniques, as a result those techniques are inefficient to detect the spam mails. In this paper we have proposed a new method to detect the image spam. Here the image features are extracted by using RGB histogram, HSV histogram and combination of both RGB and HSV histogram. Based on the optimized image feature set classification is done by using k- Nearest Neighbor(k-NN) algorithm. Experimental result shows that our method has achieved better accuracy. From the result it is known that combination of RGB and HSV histogram with k-NN algorithm gives the best accuracy in spam detection.

Keywords: File Type, HSV Histogram, k-NN, RGB Histogram, Spam Detection.

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Authors: Wojciech P. Gajewski

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The problem of spam has been seriously troubling the Internet community during the last few years and currently reached an alarming scale. Observations made at CERN (European Organization for Nuclear Research located in Geneva, Switzerland) show that spam mails can constitute up to 75% of daily SMTP traffic. A naïve Bayesian classifier based on a Bag Of Words representation of an email is widely used to stop this unwanted flood as it combines good performance with simplicity of the training and classification processes. However, facing the constantly changing patterns of spam, it is necessary to assure online adaptability of the classifier. This work proposes combining such a classifier with another NBC (naïve Bayesian classifier) based on pairs of adjacent words. Only the latter will be retrained with examples of spam reported by users. Tests are performed on considerable sets of mails both from public spam archives and CERN mailboxes. They suggest that this architecture can increase spam recall without affecting the classifier precision as it happens when only the NBC based on single words is retrained.

Keywords: Text classification, naïve Bayesian classification, spam, email.

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Authors: Jatinderkumar R. Saini, Apurva A. Desai

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In recent times, the problem of Unsolicited Bulk Email (UBE) or commonly known as Spam Email, has increased at a tremendous growth rate. We present an analysis of survey based on classifications of UBE in various research works. There are many research instances for classification between spam and non-spam emails but very few research instances are available for classification of spam emails, per se. This paper does not intend to assert some UBE classification to be better than the others nor does it propose any new classification but it bemoans the lack of harmony on number and definition of categories proposed by different researchers. The paper also elaborates on factors like intent of spammer, content of UBE and ambiguity in different categories as proposed in related research works of classifications of UBE.

Keywords: E-mail, Scams, Spam Email, Unsolicited Bulk Email(UBE)

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Authors: Raheleh Kholghi, Soheil Behnam Roudsari, Alireza Nemaney Pour

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Spam mails are unwanted mails sent to large number of users. Spam mails not only consume the network resources, but cause security threats as well. This paper proposes an efficient technique to detect, and to prevent spam mail in the sender side rather than the receiver side. This technique is based on a counter set on the sender server. When a mail is transmitted to the server, the mail server checks the number of the recipients based on its counter policy. The counter policy performed by the mail server is based on some pre-defined criteria. When the number of recipients exceeds the counter policy, the mail server discontinues the rest of the process, and sends a failure mail to sender of the mail; otherwise the mail is transmitted through the network. By using this technique, the usage of network resources such as bandwidth, and memory is preserved. The simulation results in real network show that when the counter is set on the sender side, the time required for spam mail detection is 100 times faster than the time the counter is set on the receiver side, and the network resources are preserved largely compared with other anti-spam mail techniques in the receiver side.

Keywords: Anti-spam, Mail server, Sender side, Spam mail

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Authors: Shao Bo Cheng, Yong-Jin Han, Se Young Park, Seong-Bae Park

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Consumer-to-Consumer (C2C) E-commerce has been growing at a very high speed in recent years. Since identical or nearly-same kinds of products compete one another by relying on keyword search in C2C E-commerce, some sellers describe their products with spam keywords that are popular but are not related to their products. Though such products get more chances to be retrieved and selected by consumers than those without spam keywords, the spam keywords mislead the consumers and waste their time. This problem has been reported in many commercial services like ebay and taobao, but there have been little research to solve this problem. As a solution to this problem, this paper proposes a method to classify whether keywords of a product are spam or not. The proposed method assumes that a keyword for a given product is more reliable if the keyword is observed commonly in specifications of products which are the same or the same kind as the given product. This is because that a hierarchical category of a product in general determined precisely by a seller of the product and so is the specification of the product. Since higher layers of the hierarchical category represent more general kinds of products, a reliable degree is differently determined according to the layers. Hence, reliable degrees from different layers of a hierarchical category become features for keywords and they are used together with features only from specifications for classification of the keywords. Support Vector Machines are adopted as a basic classifier using the features, since it is powerful, and widely used in many classification tasks. In the experiments, the proposed method is evaluated with a golden standard dataset from Yi-han-wang, a Chinese C2C E-commerce, and is compared with a baseline method that does not consider the hierarchical category. The experimental results show that the proposed method outperforms the baseline in F1-measure, which proves that spam keywords are effectively identified by a hierarchical category in C2C E-commerce.

Keywords: Spam Keyword, E-commerce, keyword features, spam filtering.

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Authors: N. Arulanand, K. Premalatha

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Bloom filter is a probabilistic and memory efficient data structure designed to answer rapidly whether an element is present in a set. It tells that the element is definitely not in the set but its presence is with certain probability. The trade-off to use Bloom filter is a certain configurable risk of false positives. The odds of a false positive can be made very low if the number of hash function is sufficiently large. For spam detection, weight is attached to each set of elements. The spam weight for a word is a measure used to rate the e-mail. Each word is assigned to a Bloom filter based on its weight. The proposed work introduces an enhanced concept in Bloom filter called Bin Bloom Filter (BBF). The performance of BBF over conventional Bloom filter is evaluated under various optimization techniques. Real time data set and synthetic data sets are used for experimental analysis and the results are demonstrated for bin sizes 4, 5, 6 and 7. Finally analyzing the results, it is found that the BBF which uses heuristic techniques performs better than the traditional Bloom filter in spam detection.

Keywords: Cuckoo search algorithm, levy’s flight, metaheuristic, optimal weight.

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Authors: Thanh Nguyen, Andrei Doncescu, Pierre Siegel

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Classification is an important data mining technique and could be used as data filtering in artificial intelligence. The broad application of classification for all kind of data leads to be used in nearly every field of our modern life. Classification helps us to put together different items according to the feature items decided as interesting and useful. In this paper, we compare two classification methods Naïve Bayes and ADTree use to detect spam e-mail. This choice is motivated by the fact that Naive Bayes algorithm is based on probability calculus while ADTree algorithm is based on decision tree. The parameter settings of the above classifiers use the maximization of true positive rate and minimization of false positive rate. The experiment results present classification accuracy and cost analysis in view of optimal classifier choice for Spam Detection. It is point out the number of attributes to obtain a tradeoff between number of them and the classification accuracy.

Keywords: Classification, data mining, spam filtering, naive Bayes, decision tree.

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Authors: Yi Yu, Vu Xuan Nguyen, Gaoxi Xiao

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Opinion formation in complex social networks may exhibit complex system dynamics even when based on some simplest system evolution models. An interesting and important issue is the effects of the initial state on the final steady-state opinion distribution. By carrying out extensive simulations and providing necessary discussions, we show that, while different initial opinion distributions certainly make differences to opinion evolution in social systems without noises, in systems with noises, given enough time, different initial states basically do not contribute to making any significant differences in the final steady state. Instead, it is the basal distribution of the preferred opinions that contributes to deciding the final state of the systems. We briefly explain the reasons leading to the observed conclusions. Such an observation contradicts with a long-term belief on the roles of system initial state in opinion formation, demonstrating the dominating role that opinion mutation can play in opinion formation given enough time. The observation may help to better understand certain observations of opinion evolution dynamics in real-life social networks.

Keywords: Opinion formation, Deffuant model, opinion mutation, consensus making.

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Authors: Dim En Nyaung, Thin Lai Lai Thein

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Due to the rapid increase of Internet, web opinion sources dynamically emerge which is useful for both potential customers and product manufacturers for prediction and decision purposes. These are the user generated contents written in natural languages and are unstructured-free-texts scheme. Therefore, opinion mining techniques become popular to automatically process customer reviews for extracting product features and user opinions expressed over them. Since customer reviews may contain both opinionated and factual sentences, a supervised machine learning technique applies for subjectivity classification to improve the mining performance. In this paper, we dedicate our work is the task of opinion summarization. Therefore, product feature and opinion extraction is critical to opinion summarization, because its effectiveness significantly affects the identification of semantic relationships. The polarity and numeric score of all the features are determined by Senti-WordNet Lexicon. The problem of opinion summarization refers how to relate the opinion words with respect to a certain feature. Probabilistic based model of supervised learning will improve the result that is more flexible and effective.

Keywords: Opinion Mining, Opinion Summarization, Sentiment Analysis, Text Mining.

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Authors: Yu Wang

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Nowadays, Internet enables its users to share the information online and to interact with others. Facing with numerous information, these Internet users are confused and begin to rely on the opinion leaders’ recommendations. The online opinion leaders are the individuals who have professional knowledge, who utilize the online channels to spread word-of-mouth information and who can affect the attitudes or even the behavior of their followers to some degree. Because utilizing the online opinion leaders is seen as an important approach to affect the potential consumers, how to identify them has become one of the hottest topics in the related field. Hence, in this article, the concepts and characteristics are introduced, and the researches related to identifying opinion leaders are collected and divided into three categories. Finally, the implications for future studies are provided.

Keywords: Online opinion leaders, user attributes analysis, text mining analysis, network structure analysis.

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Authors: Leili Nosrati, Alireza Nemaney Pour

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Although e-mail is the most efficient and popular communication method, unwanted and mass unsolicited e-mails, also called spam mail, endanger the existence of the mail system. This paper proposes a new algorithm called Dynamic Weighted Majority Concept Drift Detection (DWM-CDD) for content-based filtering. The design purposes of DWM-CDD are first to accurate the performance of the previously proposed algorithms, and second to speed up the time to construct the model. The results show that DWM-CDD can detect both sudden and gradual changes quickly and accurately. Moreover, the time needed for model construction is less than previously proposed algorithms.

Keywords: Concept drift, Content-based filtering, E-mail, Spammail.

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Authors: Khairullah Khan, Baharum B. Baharudin, Aurangzeb Khan, Fazal_e_Malik

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Opinion extraction about products from customer reviews is becoming an interesting area of research. Customer reviews about products are nowadays available from blogs and review sites. Also tools are being developed for extraction of opinion from these reviews to help the user as well merchants to track the most suitable choice of product. Therefore efficient method and techniques are needed to extract opinions from review and blogs. As reviews of products mostly contains discussion about the features, functions and services, therefore, efficient techniques are required to extract user comments about the desired features, functions and services. In this paper we have proposed a novel idea to find features of product from user review in an efficient way. Our focus in this paper is to get the features and opinion-oriented words about products from text through auxiliary verbs (AV) {is, was, are, were, has, have, had}. From the results of our experiments we found that 82% of features and 85% of opinion-oriented sentences include AVs. Thus these AVs are good indicators of features and opinion orientation in customer reviews.

Keywords: Classification, Customer Reviews, Helping Verbs, Opinion Mining.

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Authors: Faiza Belbachir, Thibault Schienhinski

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The massive development of online social networks allows users to post and share their opinions on various topics. With this huge volume of opinion, it is interesting to extract and interpret these information for different domains, e.g., product and service benchmarking, politic, system of recommendation. This is why opinion detection is one of the most important research tasks. It consists on differentiating between opinion data and factual data. The difficulty of this task is to determine an approach which returns opinionated document. Generally, there are two approaches used for opinion detection i.e. Lexical based approaches and Machine Learning based approaches. In Lexical based approaches, a dictionary of sentimental words is used, words are associated with weights. The opinion score of document is derived by the occurrence of words from this dictionary. In Machine learning approaches, usually a classifier is trained using a set of annotated document containing sentiment, and features such as n-grams of words, part-of-speech tags, and logical forms. Majority of these works are based on documents text to determine opinion score but dont take into account if these texts are really correct. Thus, it is interesting to exploit other information to improve opinion detection. In our work, we will develop a new way to consider the opinion score. We introduce the notion of trust score. We determine opinionated documents but also if these opinions are really trustable information in relation with topics. For that we use lexical SentiWordNet to calculate opinion and trust scores, we compute different features about users like (numbers of their comments, numbers of their useful comments, Average useful review). After that, we combine opinion score and trust score to obtain a final score. We applied our method to detect trust opinions in TRIPADVISOR collection. Our experimental results report that the combination between opinion score and trust score improves opinion detection.

Keywords: Tripadvisor, Opinion detection, SentiWordNet, trust score.

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Authors: Jatinderkumar R. Saini, Apurva A. Desai

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Email has become a fast and cheap means of online communication. The main threat to email is Unsolicited Bulk Email (UBE), commonly called spam email. The current work aims at identification of unigrams in more than 2700 UBE that advertise body-enhancement drugs. The identification is based on the requirement that the unigram is neither present in dictionary, nor is a slang term. The motives of the paper are many fold. This is an attempt to analyze spamming behaviour and employment of wordmutation technique. On the side-lines of the paper, we have attempted to better understand the spam, the slang and their interplay. The problem has been addressed by employing Tokenization technique and Unigram BOW model. We found that the non-lexicon words constitute nearly 66% of total number of lexis of corpus whereas non-slang words constitute nearly 2.4% of non-lexicon words. Further, non-lexicon non-slang unigrams composed of 2 lexicon words, form more than 71% of the total number of such unigrams. To the best of our knowledge, this is the first attempt to analyze usage of non-lexicon non-slang unigrams in any kind of UBE.

Keywords: Body Enhancement, Lexicon, Medicinal, Slang, Unigram, Unsolicited Bulk e-mail (UBE)

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Authors: Dilip Singh Sisodia, Shrish Verma

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In this paper we are presenting some spamming techniques their behaviour and possible solutions. We have analyzed how Spammers enters into online social networking sites (OSNSs) to target them and diverse techniques used by them for this purpose. Spamming is very common issue in present era of Internet especially through Online Social Networking Sites (like Facebook, Twitter, and Google+ etc.). Spam messages keep wasting Internet bandwidth and the storage space of servers. On social networking sites; spammers often disguise themselves by creating fake accounts and hijacking user’s accounts for personal gains. They behave like normal user and they continue to change their spamming strategy. Following spamming techniques are discussed in this paper like clickjacking, social engineered attacks, cross site scripting, URL shortening, and drive by download. We have used elgg framework for demonstration of some of spamming threats and respective implementation of solutions.

Keywords: Online social networking sites, spam attacks, Internet, clickjacking/likejacking, drive-by-download, URL shortening, cross site scripting, socially engineered attacks, elgg framework.

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Authors: Amalia Triantafillidou, Georgios Lappas, Prodromos Yannas, Alexandros Kleftodimos

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In this paper, an experiment was conducted to assess the impact of online deliberation on citizens’ attitudes. Specifically, this research compared pre and post deliberation opinions of participants who deliberated online via an asynchronous platform regarding the issue of political opinion polls. Results indicate that online deliberation had a positive effect on citizens’ attitudes since it was found that following deliberation participants changed their views regarding public opinion polls. Specifically, online deliberation improved discussants perceptions regarding the reliability of polls, while suppressing their negative views about the misuse of polls by media, polling organizations and politicians.

Keywords: Online deliberation, attitudes change, opinion polls, e-democracy.

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Authors: Najiba Ouled Omar, Azza Harbaoui, Henda Ben Ghezala

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Current research practices sentiment analysis with a focus on social networks, DEfi Fouille de Texte (DEFT) (Text Mining Challenge) evaluation campaign focuses on opinion mining and sentiment analysis on social networks, especially social network Twitter. It aims to confront the systems produced by several teams from public and private research laboratories. DEFT offers participants the opportunity to work on regularly renewed themes and proposes to work on opinion mining in several editions. The purpose of this article is to scrutinize and analyze the works relating to opinions mining and sentiment analysis in the Twitter social network realized by DEFT. It examines the tasks proposed by the organizers of the challenge and the methods used by the participants.

Keywords: Opinion mining, sentiment analysis, emotion, polarity, annotation, OSEE, figurative language, DEFT, Twitter, Tweet.

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Authors: A. M. H. Elyasir, K. S. M. Anbananthen

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The internet is growing larger and becoming the most popular platform for the people to share their opinion in different interests. We choose the education domain specifically comparing some Malaysian universities against each other. This comparison produces benchmark based on different criteria shared by the online users in various online resources including Twitter, Facebook and web pages. The comparison is accomplished using opinion mining framework to extract, process the unstructured text and classify the result to positive, negative or neutral (polarity). Hence, we divide our framework to three main stages; opinion collection (extraction), unstructured text processing and polarity classification. The extraction stage includes web crawling, HTML parsing, Sentence segmentation for punctuation classification, Part of Speech (POS) tagging, the second stage processes the unstructured text with stemming and stop words removal and finally prepare the raw text for classification using Named Entity Recognition (NER). Last phase is to classify the polarity and present overall result for the comparison among the Malaysian universities. The final result is useful for those who are interested to study in Malaysia, in which our final output declares clear winners based on the public opinions all over the web.

Keywords: Entity Recognition, Education Domain, Opinion Mining, Unstructured Text.

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Authors: D. I. De Souza, G. P. Azevedo, P. Duarte

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In this paper the main objective is to analyze the quality of service of the bus companies operating in the city of Campos, located in the state of Rio de Janeiro, Brazil. This analysis, based on the opinion of the bus customers, will help to determine their degree of satisfaction with the service provided by the bus companies. The result of this assessment shows that the bus customers are displeased with the quality of service supplied by the bus companies. Therefore, it is necessary to identify alternative solutions to minimize the consequences of the main problems related to customers- dissatisfaction identified in our evaluation and to help the bus companies operating in Campos better fulfill their riders- needs.

Keywords: PubicTransportation, Quality of Service, Riders'Opinion, Bus Companies

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Authors: William Xiu Shun Wong, Myungsu Lim, Yoonjin Hyun, Chen Liu, Seongi Choi, Dasom Kim, Kee-Young Kwahk, Namgyu Kim

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Recently, many users have begun to frequently share their opinions on diverse issues using various social media. Therefore, numerous governments have attempted to establish or improve national policies according to the public opinions captured from various social media. In this paper, we indicate several limitations of the traditional approaches to analyze public opinion on science and technology and provide an alternative methodology to overcome these limitations. First, we distinguish between the science and technology analysis phase and the social issue analysis phase to reflect the fact that public opinion can be formed only when a certain science and technology is applied to a specific social issue. Next, we successively apply a start list and a stop list to acquire clarified and interesting results. Finally, to identify the most appropriate documents that fit with a given subject, we develop a new logical filter concept that consists of not only mere keywords but also a logical relationship among the keywords. This study then analyzes the possibilities for the practical use of the proposed methodology thorough its application to discover core issues and public opinions from 1,700,886 documents comprising SNS, blogs, news, and discussions.

Keywords: Big data, social network analysis, text mining, topic modeling.

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Authors: Sedat Cereci

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The original idea for a feature film may come from a writer, director or a producer. Director is the person responsible for the creative aspects, both interpretive and technical, of a motion picture production in a film. Director may be shot discussing his project with his or her cowriters, members of production staff, and producer, and director may be shown selecting locales or constructing sets. All these activities provide, of course, ways of externalizing director-s ideas about the film. A director sometimes pushes both the film image and techniques of narration to new artistic limits, but main responsibility of director is take the spectator to an original opinion in his philosophical approach. Director tries to find an artistic angle in every scene and change screenplay into an effective story and sets his film on a spiritual and philosophical base.

Keywords: Director, role, film, approach, opinion.

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Authors: D. I. De Souza, D. Kipper, G. P. Azevedo

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The main objective of this work is to compare the quality of service of the bus companies operating in the city of Rio Branco, located in the state of Acre with the quality of service of the bus companies operating in the city of Campos, situated in the state of Rio de Janeiro, both cities in Brazil. This comparison, based on the opinion of the bus users, will determine their degree of satisfaction with the service available in both cities. The outcome of this evaluation shows the users unhappy with the quality of the service provided by the bus companies operating in both cities and the need to identify alternative solutions that may minimize the consequences caused by the main problems detected in this work. With these alternatives available, the bus companies will be able to better understand the needs of their customers in terms of manpower, service cost, time schedule, etc.

Keywords: PubicTransportation, Quality of Service, Riders' Opinion, Bus Companies

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Authors: Nurettin Simsek, Özlem Cakır

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The aim of this study is to point out whether personalization of mathematical word problems could affect student achievement or not. The research was applied on two-grades students at spring semester 2008-2009. Before the treatment, students personal data were taken and given to the computer. During the treatment, paper-based personalized problems and paper-based non personalized problems were prepared by computer as the same problems and then these problems were given to students. At the end of the treatment, students- opinion was taken. As a result of this research, it was found out that there were no significant differences between learners through personalized or non-personalized materials, and also there were no significant differences between gender through personalized and non-personalized problems. However, opinion of students was highly positive through the personalized problems.

Keywords: Personalization, word problem, computer aided personalization.

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Authors: Ibrahim A. Alkali

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There is no reservation on the outstanding contribution of patient families in restoration of hospitalised patients, hence their consideration as essential component of hospital ward regimen. The psychological and emotional support a patient requires has been found to be solely provided by the patient’s family. However, consideration of their presence as one of the major functional requirements of an inpatient setting design have always been a source of disquiet, especially in developing countries where policies, norms and protocols of healthcare administration have no consideration for the patients’ family. This have been a major challenge to the hospital ward facilities, a concern for the hospital administration and patient management. The study therefore is aimed at obtaining a consensus opinion on the best approach for family integration in the design of an inpatient setting.  A one day visioning charrette involving Architects, Nurses, Medical Doctors, Healthcare assistants and representatives from the Patient families was conducted with the aim of arriving at a consensus opinion on practical design approach for sustainable family integration. Patient’s family are found to be decisive character of hospital ward regimen that cannot be undermined. However, several challenges that impede family integration were identified and subsequently a recommendation for an ideal approach. This will serve as a guide to both architects and hospital management in implementing much desired Patient and Family Centred Care.

Keywords: Caregiving, Inpatient setting, Integration, Patient Families.

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Authors: Salima Behdenna, Fatiha Barigou, Ghalem Belalem

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Due to the quick increase in the volume of Arabic opinions posted on various social media, Arabic sentiment analysis has become one of the most important areas of research. Compared to English, there is very little works on Arabic sentiment analysis, in particular aspect-based sentiment analysis (ABSA). In ABSA, aspect extraction is the most important task. In this paper, we propose a semantic ABSA approach for standard Arabic reviews to extract explicit aspect terms and identify the polarity of the extracted aspects. The proposed approach was evaluated using HAAD datasets. Experiments showed that the proposed approach achieved a good level of performance compared with baseline results. The F-measure was improved by 19% for the aspect term extraction tasks and 55% aspect term polarity task.

Keywords: Sentiment analysis, opinion mining, Arabic, aspect level, opinion, polarity.

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Authors: Chothmal, Basant Agarwal

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Sentiment analysis means to classify a given review document into positive or negative polar document. Sentiment analysis research has been increased tremendously in recent times due to its large number of applications in the industry and academia. Sentiment analysis models can be used to determine the opinion of the user towards any entity or product. E-commerce companies can use sentiment analysis model to improve their products on the basis of users’ opinion. In this paper, we propose a new One-class Support Vector Machine (One-class SVM) based sentiment analysis model for movie review documents. In the proposed approach, we initially extract features from one class of documents, and further test the given documents with the one-class SVM model if a given new test document lies in the model or it is an outlier. Experimental results show the effectiveness of the proposed sentiment analysis model.

Keywords: Feature selection methods, Machine learning, NB, One-class SVM, Sentiment Analysis, Support Vector Machine.

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