Serendio: Simple and Practical lexicon based approach to Sentiment Analysis
Published 1 June 2013
Prabu palanisamy, Vineet Yadav, Harsha Elchuri
Citations66
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
This paper describes the system developed by the Serendio team for the SemEval-2013 Task 2 competition (Task A), which uses a lexicon based approach for discovering sentiments and yields an F-score of 0.8004 on the test dataset.
Abstract
This paper describes the system developed by the Serendio team for the SemEval-2013 Task 2 competition (Task A). We use a lexicon based approach for discovering sentiments. Our lexicon is built from the Serendio taxonomy. The Serendio taxonomy consists of positive, negative, negation, stop words and phrases. A typical tweet contains word variations, emoticons, hashtags etc. We use preprocessing steps such as stemming, emoticon detection and normalization, exaggerated word shortening and hashtag detection. After the preprocessing, the lexicon-based system classifies the tweets as positive or negative based on the contextual sentiment orientation of the words. Our system yields an F-score of 0.8004 on the test dataset. 1
Keywords
Computer Science
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.
now publishers, Inc. eBooksOpinion Mining and Sentiment Analysis
6,745 Citations2008Bo Pang, Lillian Lee
Meeting of the Association for Computational LinguisticsThumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews
3,654 Citations2002Peter Peter, Turney
Encyclopedia of Machine Learning and Data MiningSentiment Analysis and Opinion Mining
3,319 Citations2017Lei Zhang, Bing Liu
NLTK
3,299 Citations2002Edward Loper, Steven Bird
The Natural Language Toolkit has been rewritten, simplifying many linguistic data structures and taking advantage of recent enhancements in the Python language.
Computational LinguisticsLexicon-Based Methods for Sentiment Analysis
3,255 Citations2011Maite Taboada, Julian Brooke +3 more
The Semantic Orientation CALculator (SO-CAL) uses dictionaries of words annotated with their semantic orientation (polarity and strength), and incorporates intensification and negation, and is applied to the polarity classification task.
SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining.
2,652 Citations2010Stefano Baccianella, Andrea Esuli +1 more
This work discusses SENTIWORDNET 3.0, a lexical resource explicitly devised for supporting sentiment classification and opinion mining applications, and reports on the improvements concerning aspect (b) that it embodies with respect to version 1.0.
SENTIWORDNET: A Publicly Available Lexical Resource for Opinion Mining
2,489 Citations2006Andrea Esuli, Fabrizio Sebastiani
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.
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.
ACM Transactions on Information SystemsMeasuring praise and criticism
1,509 Citations2003Peter D. Turney, Michael L. Littman
This article introduces a method for inferring the semantic orientation of a word from its statistical association with a set of positive and negative paradigm words, based on two different statistical measures of word association.
A holistic lexicon-based approach to opinion mining
1,388 Citations2008Xiaowen Ding, Bing Liu +1 more
This paper proposes a holistic lexicon-based approach to solving the problem of determining the semantic orientations (positive, negative or neutral) of opinions expressed on product features in reviews by exploiting external evidences and linguistic conventions of natural language expressions.
Proceedings of the International AAAI Conference on Web and Social MediaTwitter Sentiment Analysis: The Good the Bad and the OMG!
1,243 Citations2021Efthymios Kouloumpis, Theresa Wilson +1 more
Named Entity Recognition in Tweets: An Experimental Study
1,203 Citations2011Alan Ritter, Sam Clark +1 more
The novel T-ner system doubles F1 score compared with the Stanford NER system, and leverages the redundancy inherent in tweets to achieve this performance, using LabeledLDA to exploit Freebase dictionaries as a source of distant supervision.
Enhanced Sentiment Learning Using Twitter Hashtags and Smileys
705 Citations2010Dmitry Davidov, Oren Tsur +1 more
A supervised sentiment classification framework which is based on data from Twitter, a popular microblogging service, is proposed, utilizing 50 Twitter tags and 15 smileys as sentiment labels, allowing identification and classification of diverse sentiment types of short texts.
Open domain event extraction from twitter
602 Citations2012Alan Ritter, Mausam Mausam +2 more
TwiCal is described-- the first open-domain event-extraction and categorization system for Twitter, and a novel approach for discovering important event categories and classifying extracted events based on latent variable models is presented.
Lecture notes in computer scienceSentiment Knowledge Discovery in Twitter Streaming Data
580 Citations2010Albert Bifet, Eibe Frank
To deal with streaming unbalanced classes, a sliding window Kappa statistic is proposed for evaluation in time-changing data streams, and a study on Twitter data is performed using learning algorithms for data streams.
Sentiment analysis of blogs by combining lexical knowledge with text classification
505 Citations2009Prem Melville, Wojciech Gryc +1 more
This paper presents a unified framework in which one can use background lexical information in terms of word-class associations, and refine this information for specific domains using any available training examples, and shows that this approach performs better than using background knowledge or training data in isolation.
Learning subjective nouns using extraction pattern bootstrapping
499 Citations2003Ellen Riloff, Janyce Wiebe +1 more
The goal of the research is to develop a system that can distinguish subjective sentences from objective sentences, and a Naive Bayes classifier is trained using the subjective nouns, discourse features, and subjectivity clues identified in prior research.
Lexical Normalisation of Short Text Messages: Makn Sens a #twitter
455 Citations2011Bo Han, Timothy Baldwin
This paper targets out-of-vocabulary words in short text messages and proposes a method for identifying and normalising ill-formed words, which achieves state- of-the-art performance over an SMS corpus and a novel dataset based on Twitter.
SemEval-2016 Task 4: Sentiment Analysis in Twitter
433 Citations2016Preslav Nakov, Sara Rosenthal +3 more
The fourth year of the SemEval-2016 Task 4 comprises five subtasks, three of which represent a significant departure from previous editions, and the task continues to be very popular, attracting a total of 43 teams.
Fully automatic lexicon expansion for domain-oriented sentiment analysis
411 Citations2006Hiroshi Kanayama, Tetsuya Nasukawa
This paper proposes an unsupervised lexicon building method for the detection of polar clauses, which convey positive or negative aspects in a specific domain, and its method is robust for corpora in diverse domains and for the size of the initial lexicon.
Classifying sentiment in microblogs
324 Citations2010Adam Bermingham, Alan F. Smeaton
Surprisingly, it is found classifying sentiment in microblogs easier than in blogs and a number of observations pertaining to the challenge of supervised learning for sentiment analysis in micro blogs are made.
Building Lexicon for Sentiment Analysis from Massive Collection of HTML Documents
268 Citations2007Nobuhiro Kaji, Masaru Kitsuregawa
The key idea is to develop the structural clues so that it achieves extremely high precision at the cost of recall, and build lexicon from the extracted polar sentences.
Publication Server of the Institute for German Language (Institute for German Language)A survey on the role of negation in sentiment analysis
212 Citations2010Michael Wiegand, Alexandra Balahur +3 more
This paper presents various computational approaches modeling negation in sentiment analysis and focuses on aspects, such as level of representation used for sentiment analysis, negation word detection and scope of negation.
Q-WordNet: Extracting Polarity from WordNet Senses
55 Citations2010Rodrigo Agerri, Ana Garcı́a-Serrano
Q-WordNet, a lexical resource consisting of WordNet senses automatically annotated by positive and negative polarity, can be used as a starting point for data-driven approaches in sentiment analysis and shows an improvement with respect to previous approaches.