Matrix Factorization Techniques for Recommender Systems

This paper presents our experimental work on a new kind of domain specific feature-based heuristic for aspect-level sentiment analysis of movie reviews. We have devised an aspect oriented scheme that analyses the textual reviews of a movie and assign it a sentiment label on each aspect. The scores on each aspect from multiple reviews are then aggregated and a net sentiment profile of the movie is generated on all parameters. We have used a SentiWordNet based scheme with two different linguistic feature selections comprising of adjectives, adverbs and verbs and n-gram feature extraction. We have also used our SentiWordNet scheme to compute the document-level sentiment for each movie reviewed and compared the results with results obtained using Alchemy API. The sentiment profile of a movie is also compared with the document-level sentiment result. The results obtained show that our scheme produces a more accurate and focused sentiment profile than the simple document-level sentiment analysis.

Sentiment analysis of movie reviews: A new feature-based heuristic for aspect-level sentiment classification

With the advent of Internet, people actively express their opinions about products, services, events, political parties, etc., in social media, blogs, and website comments. The amount of research work on sentiment analysis is growing explosively. However, the majority of research efforts are devoted to English-language data, while a great share of information is available in other languages. We present a state-of-the-art review on multilingual sentiment analysis. More importantly, we compare our own implementation of existing approaches on common data. Precision observed in our experiments is typically lower than the one reported by the original authors, which we attribute to the lack of detail in the original presentation of those approaches. Thus, we compare the existing works by what they really offer to the reader, including whether they allow for accurate implementation and for reliable reproduction of the reported results.

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Multilingual Sentiment Analysis: State of the Art and Independent Comparison of Techniques.

Sentiment analysis of Twitter data during critical events through Bayesian networks classifiers

The idiosyncrasy of the Web has, in the last few years, been altered by Web 2.0 technologies and applications and the advent of the so-called Social Web. While users were merely information consumers in the traditional Web, they play a much more active role in the Social Web since they are now also data providers. The mass involved in the process of creating Web content has led many public and private organizations to focus their attention on analyzing this content in order to ascertain the general public’s opinions as regards a number of topics. Given the current Web size and growth rate, automated techniques are essential if practical and scalable solutions are to be obtained. Opinion mining is a highly active research field that comprises natural language processing, computational linguistics and text analysis techniques with the aim of extracting various kinds of added-value and informational elements from users’ opinions. However, current opinion mining approaches are hampered by a number of drawbacks such as the absence of semantic relations between concepts in feature search processes or the lack of advanced mathematical methods in sentiment analysis processes. In this paper we propose an innovative opinion mining methodology that takes advantage of new Semantic Web-guided solutions to enhance the results obtained with traditional natural language processing techniques and sentiment analysis processes. The main goals of the proposed methodology are: (1) to improve feature-based opinion mining by using ontologies at the feature selection stage, and (2) to provide a new vector analysis-based method for sentiment analysis. The methodology has been implemented and thoroughly tested in a real-world movie review-themed scenario, yielding very promising results when compared with other conventional approaches.

Feature-based opinion mining through ontologies

This paper presents our experimental work on performance evaluation of the SentiWordNet approach for document-level sentiment classification of Movie reviews and Blog posts. We have implemented SentiWordNet approach with different variations of linguistic features, scoring schemes and aggregation thresholds. We used two pre-existing large datasets of Movie Reviews and two Blog post datasets on revolutionary changes in Libya and Tunisia. We have computed sentiment polarity and also its strength for both movie reviews and blog posts. The paper also presents an evaluative account of performance of the SentiWordNet approach with two popular machine learning approaches: Naive Bayes and SVM for sentiment classification. The comparative performance of the approaches for both movie reviews and blog posts is illustrated through standard performance evaluation metrics of Accuracy, F-measure and Entropy.

Sentiment analysis of Movie reviews and Blog posts

This paper presents our experimental results on performance evaluation of all the three approaches for document-level sentiment classification. We have implemented two Machine Learning based classifiers (Naive Bayes and SVM), the Unsupervised Semantic Orientation approach (SO-PMI-IR algorithm) and the SentiWordNet approaches for sentiment classification of movie reviews. We used two pre-existing large datasets and collected one of moderate size on our own. The paper primarily makes two useful contributions: (a) it presents a comprehensive evaluative account of performance of all the three available approaches on use with movie reviews, and (b) it presents a new modified Adjective+Adverb combine scheme of SentiWordNet approach.

Sentiment analysis of textual reviews; Evaluating machine learning, unsupervised and SentiWordNet approaches

This paper presents our experimental work on evaluation of Machine Learning based classification approaches (Naive Bayes and SVM) with the Unsupervised Semantic Orientation based SO-PMI-IR algorithm for sentiment analysis of movie review texts We have used both pre-existing data sets and our own dataset collection comprising of a large number of user reviews for Hindi movies The Naive Bayes and SVM approaches were implemented in multiple folds The results, in addition to presenting a detailed comparative view of these techniques, demonstrate that with suitable selection of features the Naive Bayes algorithm performs reasonably well and at times matches the popularly believed superior performance level of SVM, at least for sentiment analysis task The SO-PMI-IR algorithm produces substantially accurate sentiment classification without the requirement of any prior training The accuracy of SO-PMI-IR however depends on POS tags used as features and thresholding/ aggregation schemes used

Evaluating Machine Learning and Unsupervised Semantic Orientation approaches for sentiment analysis of textual reviews

This paper presents analytical outcomes of scientometric mapping of research work done on the important emerging area of ‘Recommender Systems. Research on ‘Recommender Systems’ started during last few years and within a short span of time has gained tremendous momentum. It is now considered as important emerging areas of research in computational sciences and related disciplines. We have analyzed the research output data on ‘Recommender Systems’ during 1991-2015 indexed in the Web of Knowledge. The analysis maps comprehensively the parameters of total output, growth of output, authorship and country-level collaboration patterns, major contributors (countries, institutions and individuals), top publication sources, thematic trends and emerging topics in the field. The paper presents an elaborate and first of its kind scientometric mapping of research on ‘Recommender Systems’.

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P. Waila

Papers

Sentiment analysis of movie reviews: A new feature-based heuristic for aspect-level sentiment classification

Sentiment analysis of Movie reviews and Blog posts

Sentiment analysis of textual reviews; Evaluating machine learning, unsupervised and SentiWordNet approaches

Evaluating Machine Learning and Unsupervised Semantic Orientation approaches for sentiment analysis of textual reviews

A Scientometric Analysis of Research in Recommender Systems