Sentiment analysis of Movie reviews and Blog posts
Published 1 February 2013
Vivek Kumar Singh, Rajesh Piryani, Ashraf Uddin, P. Waila
Citations74
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
The paper presents an evaluative account of performance of the SentiWordNet approach with two popular machine learning approaches: Naïve Bayes and SVM for sentiment classification and standard performance evaluation metrics of Accuracy, F-measure and Entropy.
Abstract
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: Naïve 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.
Keywords
Computer Science
Cambridge University Press eBooksIntroduction to Information Retrieval
10,873 Citations2008Christopher D. Manning, Prabhakar Raghavan +1 more
This textbook teaches classical and web information retrieval, including web search and the related areas of text classification and text clustering from basic concepts, making it perfect for introductory courses in information retrieval for advanced undergraduates and graduate students in computer science.
ACM Computing SurveysMachine learning in automated text categorization
7,899 Citations2002Fabrizio Sebastiani
This survey discusses the main approaches to text categorization that fall within the machine learning paradigm and discusses in detail issues pertaining to three different problems, namely, document representation, classifier construction, and classifier evaluation.
Thumbs up?
6,987 Citations2002Bo Pang, Lillian Lee +1 more
This work considers the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative, and concludes by examining factors that make the sentiment classification problem more challenging.
Meeting of the Association for Computational LinguisticsThumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews
3,654 Citations2002Peter Peter, Turney
Mining the peanut gallery
1,908 Citations2003Kushal Dave, Steve Lawrence +1 more
This work develops a method for automatically distinguishing between positive and negative reviews and draws on information retrieval techniques for feature extraction and scoring, and the results for various metrics and heuristics vary depending on the testing situation.
ArXiv.orgThumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews
1,585 Citations2002Peter D. Turney
Determining the sentiment of opinions
1,485 Citations2004Soo-Min Kim, Eduard Hovy
A system that, given a topic, automatically finds the people who hold opinions about that topic and the sentiment of each opinion and another module for determining word sentiment and another for combining sentiments within a sentence is presented.
Choice Reviews OnlineWeb data mining: exploring hyperlinks, contents, and usage data
1,193 Citations2012
Determining the semantic orientation of terms through gloss classification
388 Citations2005Andrea Esuli, Fabrizio Sebastiani
This paper presents a new method for determining the orientation of subjective terms based on the quantitative analysis of the glosses of such terms given in on-line dictionaries, and on the use of the resulting term representations for semi-supervised term classification.
arXiv (Cornell University)Unsupervised Learning of Semantic Orientation from a Hundred-Billion-Word Corpus
361 Citations2002Peter D. Turney, Michael L. Littman
This paper introduces a simple algorithm for unsupervised learning of semantic orientation from extremely large corpora by issuing queries to a Web search engine and using pointwise mutual information to analyse the results.
Sentiment Analysis: Adjectives and Adverbs are better than Adjectives Alone
330 Citations2007Farah Benamara, Carmine Cesarano +3 more
This work proposes an AAC-based sentiment analysis technique that uses a linguistic analysis of adverbs of degree that leads to higher accuracy based on Pearson correlation with human subjects and describes the results of experiments.
Evaluating Machine Learning and Unsupervised Semantic Orientation approaches for sentiment analysis of textual reviews
27 Citations2012P. Waila, Marisha Tiwari +2 more
It is demonstrated 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.
Communications in computer and information scienceCombining a Content Filtering Heuristic and Sentiment Analysis for Movie Recommendations
19 Citations2011Vivek Kumar Singh, Mousumi Mukherjee +1 more
This paper presents experimental work on a new approach to hybrid recommender systems, where the content-based approach is combined with a sentiment analysis task to improve the recommendation results.
Lecture notes in computer scienceAgent-Based Computational Modeling of Emergent Collective Intelligence
16 Citations2009Vivek Kumar Singh, Divya Gautam +2 more
Agent-based modeling of complex social behaviours by simulating social units as agents and modeling their interactions provides a new generative approach to understanding the dynamics of emergence of collective intelligence behaviours.
What does everybody know? Identifying event-specific sources from social media
14 Citations2012Debanjan Mahata, Nitin Agarwal
This work proposes a framework to distinguish the disparate sources from social media that provide extremely significant information about various events, and proposes information theoretic measures to identify “specific” sources for an event (often buried in the Long Tail) and “closer” entities (individuals, groups, organizations, places, etc.) for anevent.
Communications in computer and information scienceSentiment and Mood Analysis of Weblogs Using POS Tagging Based Approach
12 Citations2011Vivek Kumar Singh, Mousumi Mukherjee +1 more
The experimental work involves transforming the collected blog data into vector space representation, doing Parts of Speech Tagging to extract opinionated words and then applying semantic orientation approach based SO-PMI-IR algorithm for mining the sentiment and mood information contained in the blog text.