Proceedings ArticleDOI
iFeel: a system that compares and combines sentiment analysis methods
Matheus Araújo,Pollyanna Gonçalves,Meeyoung Cha,Fabrício Benevenuto +3 more
- pp 75-78
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TLDR
iFeel is a Web application that allows one to detect sentiments in any form of text including unstructured social media data and provides a single platform to compare the strengths and weaknesses of various sentiment analysis methods with a user friendly interface.Abstract:
Sentiment analysis methods are used to detect polarity in thoughts and opinions of users in online social media. As businesses and companies are interested in knowing how social media users perceive their brands, sentiment analysis can help better evaluate their product and advertisement campaigns. In this paper, we present iFeel, a Web application that allows one to detect sentiments in any form of text including unstructured social media data. iFeel is free and gives access to seven existing sentiment analysis methods: SentiWordNet, Emoticons, PANAS-t, SASA, Happiness Index, SenticNet, and SentiStrength. With iFeel, users can also combine these methods and create a new Combined-Method that achieves high coverage and F-measure. iFeel provides a single platform to compare the strengths and weaknesses of various sentiment analysis methods with a user friendly interface such as file uploading, graphical visualizing, and weight tuning.read more
Citations
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Journal ArticleDOI
Affective Computing and Sentiment Analysis
TL;DR: The emerging fields of affective computing and sentiment analysis, which leverage human-computer interaction, information retrieval, and multimodal signal processing for distilling people's sentiments from the ever-growing amount of online social data.
BookDOI
A Practical Guide to Sentiment Analysis
TL;DR: The main aim of this book is to provide a feasible research platform to ambitious researchers towards developing the practical solutions that will be indeed beneficial for the authors' society, business and future researches as well.
Proceedings Article
SenticNet 4: A Semantic Resource for Sentiment Analysis Based on Conceptual Primitives
TL;DR: SenticNet 4 overcomes limitations by leveraging on conceptual primitives automatically generated by means of hierarchical clustering and dimensionality reduction.
Posted Content
A Benchmark Comparison of State-of-the-Practice Sentiment Analysis Methods.
Filipe N. Ribeiro,Matheus Araújo,Pollyanna Gonçalves,Fabrício Benevenuto,Marcos André Gonçalves +4 more
Abstract: In the last few years thousands of scientific papers have explored sentiment analysis, several startups that measures opinions on real data have emerged, and a number of innovative products related to this theme have been developed. There are multiple methods for measuring sentiments, including lexical-based approaches and supervised machine learning methods. Despite the vast interest on the theme and wide popularity of some methods, it is unclear which method is better for identifying the polarity (i.e., positive or negative) of a message. Thus, there is a strong need to conduct a thorough apple-to-apple comparison of sentiment analysis methods, as they are used in practice, across multiple datasets originated from different data sources. Such a comparison is key for understanding the potential limitations, advantages, and disadvantages of popular methods. This study aims at filling this gap by presenting a benchmark comparison of twenty one popular sentiment analysis methods (which we call the state-of-the-practice methods). Our evaluation is based on a benchmark of twenty labeled datasets, covering messages posted on social networks, movie and product reviews, as well as opinions and comments in news articles. Our results highlight the extent to which the prediction performance of these methods varies widely across datasets. Aiming at boosting the development of this research area, we open the methods' codes and datasets used in this paper and we deploy a benchmark system, which provides an open API for accessing and comparing sentence-level sentiment analysis methods.
Proceedings Article
Breaking the News: First Impressions Matter on Online News
Julio Cesar Soares dos Rieis,Fabrício Benevenuto de Souza,Pedro O. S. Vaz de Melo,Raquel Oliveira Prates,Haewoon Kwak,Jisun An +5 more
TL;DR: It is discovered that the sentiment of the headline is strongly related to the popularity of the news and also with the dynamics of the posted comments on that particular news.
References
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Journal ArticleDOI
Development and validation of brief measures of positive and negative affect: The PANAS scales.
TL;DR: Two 10-item mood scales that comprise the Positive and Negative Affect Schedule (PANAS) are developed and are shown to be highly internally consistent, largely uncorrelated, and stable at appropriate levels over a 2-month time period.
Journal ArticleDOI
The psychological meaning of words: LIWC and computerized text analysis methods
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Proceedings Article
Measuring User Influence in Twitter: The Million Follower Fallacy
TL;DR: An in-depth comparison of three measures of influence, using a large amount of data collected from Twitter, is presented, suggesting that topological measures such as indegree alone reveals very little about the influence of a user.
Proceedings Article
SENTIWORDNET: A Publicly Available Lexical Resource for Opinion Mining
Andrea Esuli,Fabrizio Sebastiani +1 more
TL;DR: SENTIWORDNET is a lexical resource in which each WORDNET synset is associated to three numerical scores Obj, Pos and Neg, describing how objective, positive, and negative the terms contained in the synset are.
Proceedings ArticleDOI
OpinionFinder: A System for Subjectivity Analysis
Theresa Wilson,Paul Hoffmann,Swapna Somasundaran,Jason S. Kessler,Janyce Wiebe,Yejin Choi,Claire Cardie,Ellen Riloff,Siddharth Patwardhan +8 more
TL;DR: OpinionFinder is a system that performs subjectivity analysis, automatically identifying when opinions, sentiments, speculations, and other private states are present in text.