Lexicon Based Sentiment Analysis of Twitter Data

Ray, Debolina

doi:10.22214/ijraset.2017.10130

Cited by 11 publications

(12 citation statements)

References 24 publications

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“…However, short social texts such as Twitter tweets produced limited number of features [45]. Fortunately, recent studies [45][46][47][48] have proposed numerous methods on how to extend and improve lexiconbased sentiment analysis. This includes the following techniques:  Emoticons.…”

Section: Discussionmentioning

confidence: 99%

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Sentiment Analysis of Tweets on Coronavirus Disease 2019 (COVID-19) Pandemic from Metro Manila, Philippines

Garcia

2020

Cybernetics and Information Technologies

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From the outbreak of a novel COronaVIrus Disease (COVID-19) in Wuhan to the first COVID-19 case in the Philippines, Filipinos have been enthusiastically engaging on Twitter to convey their sentiments. As such, this paper aims to identify the public opinion of Filipino twitter users concerning COVID-19 in three different timelines. Toward this goal, a total of 65,396 tweets related to COVID-19 were sent to data analysis using R Statistical Software. Results show that “mask”, “health”, “lockdown”, “outbreak”, “test”, “kit”, “university”, “alcohol”, and “suspension” were some of the most frequently occurring words in the tweets. The study further investigates Filipinos’ emotions regarding COVID-19 by calculating text polarity of the dataset. To date, this is the first paper to perform sentiment analysis on tweets pertaining to COVID-19 not only in the Filipino context but worldwide as well.

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Section: Discussionmentioning

confidence: 99%

“…The expansion of lexicon-based approach though the aforesaid techniques lead to a more accurate classification of tweets [45][46][47][48]. These serve as sentiment-based features together with polarity cues (e.g., positive words, negative words, and the number of positive per negative words), and combined with unigrams (frequency of words).…”

Section: Discussionmentioning

confidence: 99%

Sentiment Analysis of Tweets on Coronavirus Disease 2019 (COVID-19) Pandemic from Metro Manila, Philippines

Garcia

2020

Cybernetics and Information Technologies

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“…Dibakar Ray et al [6] have put forward the Sentiment Analysis on twitter data using lexicon based sentiment. The analysis has used negation words, degree modifier words, capitalized words, slang words, repeated letters in a word and multiple punctuations in a word.…”

Section: B Opinion Mining On Intensifier Detectionmentioning

confidence: 99%

Sentiment Analysis on Social Media Big Data With Multiple Tweet Words

Maheswari¹,

Dhenakaran²

2019

IJITEE

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The main objective of this paper is Analyze the reviews of Social Media Big Data of E-Commerce product's. And provides helpful result to online shopping customers about the product quality and also provides helpful decision making idea to the business about the customer's mostly liking and buying products. This covers all features or opinion words, like capitalized words, sequence of repeated letters, emoji, slang words, exclamatory words, intensifiers, modifiers, conjunction words and negation words etc available in tweets. The existing work has considered only two or three features to perform Sentiment Analysis with the machine learning technique Natural Language Processing (NLP). In this proposed work familiar Machine Learning classification models namely Multinomial Naïve Bayes, Support Vector Machine, DecisionTree Classifier, and, Random Forest Classifier are used for sentiment classification. The sentiment classification is used as a decision support system for the customers and also for the business.

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“…Basically, sentiment analysis is a classification technique that categorizes the tweets in various sentiment classes. Different classification algorithms as Machine learning [9]- [10], Lexicon based [11], Ensemble classifiers [12] and Neural network based classifiers are used by researchers. Ensemble classifiers are very effective to enhance the overall efficiency of a model.…”

Section: Introductionmentioning

confidence: 99%

Analyzing impact of number of features on efficiency of hybrid model of lexicon and stack based ensemble classifier for twitter sentiment analysis using WEKA tool

Rani¹,

Gill²,

Gulia³

2021

IJEECS

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<span>Twitter is used by millions of people across the world, so the data collected from Twitter can be highly valuable for research and helpful in decision support. Here in this paper ‘Twitter US Airline data’ from Kaggle data repository is used for sentiment classification of customers’ reviews. The current research aims to implement various machine learning classifiers, Stack-based ensemble classifiers and hybrid of lexicon classifier with other classifiers. 11 different classification models are implemented for different sized feature sets. Also, all the 11 models are re-implemented by adding sentiment score of lexicon based classifier as one of the features in the feature set. Results are analyzed by varying number of input feature variables used in the classification. Four different size feature sets having 301,501, 701, and 1301 number of features are used to analyze the variations in the final findings. Chi-Square and Information gain techniques are used for feature selection. The results show that an increase in the number of features increases the accuracy up to 701 features. After that, accuracy is stable or decreases with increase in feature set size. Also, the cost of adding sentiment score of lexicon classifier to the input feature set is nominal, but the results are improved consistently. WEKA and R Studio tools are used for analysis and implementation. Accuracy and Kappa are used for representing and comparing the efficiency of models.</span>

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Lexicon Based Sentiment Analysis of Twitter Data

Cited by 11 publications

References 24 publications

Sentiment Analysis of Tweets on Coronavirus Disease 2019 (COVID-19) Pandemic from Metro Manila, Philippines

Sentiment Analysis of Tweets on Coronavirus Disease 2019 (COVID-19) Pandemic from Metro Manila, Philippines

Sentiment Analysis on Social Media Big Data With Multiple Tweet Words

Analyzing impact of number of features on efficiency of hybrid model of lexicon and stack based ensemble classifier for twitter sentiment analysis using WEKA tool

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