iFeel: a system that compares and combines sentiment analysis methods

  title={iFeel: a system that compares and combines sentiment analysis methods},
  author={Matheus Ara{\'u}jo and Pollyanna Gonçalves and M. Cha and Fabr{\'i}cio Benevenuto},
  journal={Proceedings of the 23rd International Conference on World Wide Web},
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… 
iFeel 2.0: A Multilingual Benchmarking System for Sentence-Level Sentiment Analysis
iFeel 2.0 is proposed, an online web system that implements 19 sentence-level sentiment analysis methods and allows users to easily label a dataset with all of them and can also be helpful for those interested in using sentiment analysis.
A web-based tool for Arabic sentiment analysis
A new tool that applies sentiment analysis to Arabic text tweets using a combination of parameters, which shows that the Naive Bayes machine-learning approach is the most accurate in predicting topic polarity.
An evaluation of machine translation for multilingual sentence-level sentiment analysis
Evaluating existing efforts proposed to do language specific sentiment analysis for English suggests that simply translating the input text on a specific language to English and then using one of the existing English methods can be better than the existing language specific efforts evaluated.
Piegas: A Systems for Sentiment Analysis of Tweets in Portuguese
The Piegas system determines automatically the sentiments, or polarity, of tweets written in Portuguese about a topic of interest, using a Naïve Bayes classifier to identify the sentiments of tweets.
A comparative study of machine translation for multilingual sentence-level sentiment analysis
This work evaluates existing efforts proposed to do language specific sentiment analysis with a simple yet effective baseline approach and suggests that simply translating the input text in a specific language to English and then using one of the existing best methods developed for English can be better than the existing language-specific approach evaluated.
SentiBench - a benchmark comparison of state-of-the-practice sentiment analysis methods
A benchmark comparison of twenty-four popular sentiment analysis methods, covering messages posted on social networks, movie and product reviews, as well as opinions and comments in news articles is presented, highlighting the extent to which the prediction performance of these methods varies considerably across datasets.
What Causes Wrong Sentiment Classifications of Game Reviews
Sentiment analysis is a popular technique to identify the sentiment of a piece of text. Several different domains have been targeted by sentiment analysis research, such as Twitter, movie reviews,
Sentimental analysis is the process of defining the opinion of the individual by extracting the data. Sentimental analysis is done by collecting the data from variety of resources like internet and
Applying sentiment and emotion analysis on brand tweets for digital marketing
  • Dua'a Al-Hajjar, A. Z. Syed
  • Computer Science
    2015 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT)
  • 2015
A lexicon-based approach to extracting sentiment and emotion from tweets for digital marketing purposes and shows that the accuracy of the combined approach of sentiment and emotions analysis is enhanced over the independent approaches of sentiment analysis or emotion analysis.
Sentiment Analysis Methods for Social Media
First, an overview about sentiment analysis and it's popular applications are presented, next, main methods from literature are discussed, and these methods are compared with each other highlighting advantages and limitations.


Comparing and combining sentiment analysis methods
A new method that combines existing approaches, providing the best coverage results and competitive agreement is developed and a free Web service called iFeel is presented, which provides an open API for accessing and comparing results across different sentiment methods for a given text.
Exploiting social relations for sentiment analysis in microblogging
This work proposes a Sociological Approach to handling Noisy and short Texts (SANT) for sentiment classification and presents a mathematical optimization formulation that incorporates the sentiment consistency and emotional contagion theories into the supervised learning process.
PANAS-t: A Pychometric Scale for Measuring Sentiments on Twitter
This paper proposes PANAS-t, which measures sentiments from short text updates in Twitter based on a well-established psychometric scale, PANAS (Positive and Negative Affect Schedule), and demonstrates that it can efficiently capture the expected sentiments of a wide variety of issues spanning tragedies, technology releases, political debates, and healthcare.
SENTIWORDNET: A Publicly Available Lexical Resource for Opinion Mining
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.
OpinionFinder: A System for Subjectivity Analysis
OpinionFinder is a system that performs subjectivity analysis, automatically identifying when opinions, sentiments, speculations, and other private states are present in text. Specifically,
A System for Real-time Twitter Sentiment Analysis of 2012 U.S. Presidential Election Cycle
A system for real-time analysis of public sentiment toward presidential candidates in the 2012 U.S. election as expressed on Twitter, a micro-blogging service, offers a new and timely perspective on the dynamics of the electoral process and public opinion.
SenticNet: A Publicly Available Semantic Resource for Opinion Mining
SenticNet is a publicly available resource for opinion mining built exploiting AI and Semantic Web techniques and uses dimensionality reduction to infer the polarity of common sense concepts and hence provide a public resource for mining opinions from natural language text at a semantic, rather than just syntactic, level.
Measuring User Influence in Twitter: The Million Follower Fallacy
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.
The Hourglass of Emotions
A novel biologically-inspired and psychologically-motivated emotion categorisation model that represents affective states both through labels and through four independent but concomitant affective dimensions, which can potentially describe the full range of emotional experiences that are rooted in any of us.
Towards Crowd Validation of the UK National Health Service
Online patient opinions are a very important instrument for the eective evaluation of local hospitals, hospices and mental health services but the distillation of knowledge from this unstructured