NILC_USP: An Improved Hybrid System for Sentiment Analysis in Twitter Messages

Filho, Pedro Balage; Avanço, Lucas; Pardo, Thiago Alexandre Salgueiro; Nunes, Maria das Graças Volpe

doi:10.3115/v1/s14-2074

Cited by 16 publications

(10 citation statements)

References 10 publications

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“…Moreover, their system combined three classification methods: machine learning, rule-based, and lexicon-based. Balage Filho and Pardo [51] used the SentiStrength lexicon and the SVM classifier as a machine learning method. The results obtained from the experiments showed that a hybrid system outperformed the individual classifiers, achieving an Fmeasure of 0.56 compared to 0.14, 0.448, and 0.49 obtained by the rule-based, lexicon-based, and SVM classifiers respectively.…”

Section: Twitter Sentiment Analysis Using Hybrid Methodsmentioning

confidence: 99%

A Study on Sentiment Analysis Techniques of Twitter Data

Alsaeedi¹,

Khan²

2019

ijacsa

207

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The entire world is transforming quickly under the present innovations. The Internet has become a basic requirement for everybody with the Web being utilized in every field. With the rapid increase in social network applications, people are using these platforms to voice them their opinions with regard to daily issues. Gathering and analyzing peoples' reactions toward buying a product, public services, and so on are vital. Sentiment analysis (or opinion mining) is a common dialogue preparing task that aims to discover the sentiments behind opinions in texts on varying subjects. In recent years, researchers in the field of sentiment analysis have been concerned with analyzing opinions on different topics such as movies, commercial products, and daily societal issues. Twitter is an enormously popular microblog on which clients may voice their opinions. Opinion investigation of Twitter data is a field that has been given much attention over the last decade and involves dissecting "tweets" (comments) and the content of these expressions. As such, this paper explores the various sentiment analysis applied to Twitter data and their outcomes.

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Section: Twitter Sentiment Analysis Using Hybrid Methodsmentioning

confidence: 99%

A Study on Sentiment Analysis Techniques of Twitter Data

Alsaeedi¹,

Khan²

2019

ijacsa

207

View full text Add to dashboard Cite

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“…It uses the lexicon classification through a predefined dictionary and classifies that data using machine learning methods. Most commonly used hybrid techniques include pSenti [14], SAIL [15], NILC_USP [16] and Alchemy API [17] as discussed in detail by [18]. In this research, Support Vector Machine (SVM) is selected for sentiment analysis of two pre classified sets of tweet.…”

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis of Tweets using SVM

Ahmad¹,

Aftab²,

Ali³

2017

IJCA

110

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Community's view and feedback have always proved to be the most essential and valuable resource for companies and organizations. With social media being the emerging trend among everyone, it paves way for unprecedented analysis and evaluation of various aspects for which organizations had to rely on unconventional, time consuming and error prone methods earlier. This technique of analysis directly falls under the domain of "sentiment analysis". Sentiment analysis encompasses the vast field of effective classification of user generated text under defined polarities. There are several tools and algorithms available to perform sentiment detection and analysis including supervised machine learning algorithms that perform classification on the target corpus, after getting trained with training data. Lexical techniques which performs classification on the basis of dictionary based annotated corpus and Hybrid tools which are combination of machine learning and lexicon based algorithms. In this paper we have used Support Vector Machine (SVM) for sentiment analysis in Weka. SVM is one of the widely used supervised machine learning algorithms for textual polarity detection. To analyze the performance of SVM, two pre classified datasets of tweets are used and for comparative analysis, three measures are used: Precision, Recall and F-Measure. Results are shown in the form of tables and graphs.

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“…Hybrid approaches differ in the circumstances under which lexical analysis or machine learning is preferred [19], [20] or the priority (i.e. weight) that each method is assigned if their individual performance is taken into account [21], [22]. Following this line of research, our hybrid approach utilizes the advantages of both, lexical analysis and machine learning, although giving priority to traditionally more successful lexical approach.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Approach for Emotion Detection in Support of Affective Interaction

Gievska

Koroveshovski

Chavdarova

2014

2014 IEEE International Conference on Data Mining Workshop

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Affective interaction is a new emerging area of interest for interaction designers. This research explores the potential of our hybrid approach that relies on both, lexical and machine learning techniques for detection of Ekman's six emotional categories in user's text. The initial results of the performance evaluation of the proposed hybrid approach are encouraging and comparable to related research. A demonstrative mobile application that employs the proposed approach was developed to engage the users in a dialogue that solicits their reflections on various daily events and provides appropriate affective responses.

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NILC_USP: An Improved Hybrid System for Sentiment Analysis in Twitter Messages

Cited by 16 publications

References 10 publications

A Study on Sentiment Analysis Techniques of Twitter Data

A Study on Sentiment Analysis Techniques of Twitter Data

Sentiment Analysis of Tweets using SVM

A Hybrid Approach for Emotion Detection in Support of Affective Interaction

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