Enhancing Sentiment Analysis Using Domain-Specific Lexicon: A Case Study on GST

Yadav, Subham; Sarkar, Mitali

doi:10.1109/icacci.2018.8554600

Cited by 6 publications

(4 citation statements)

References 7 publications

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“…SentiWordNet has been built based on the English lexical dictionary WordNet, whereby words are grouped into synsets (synonym sets) and each synset has been assigned with three polarity scores, representing positive, negative, and neutral classes [25]. Agarwal et al [8] used SentiWordNet alongside newspaper articles for the creation of domain-specific lexicon (financial) and discovered that some words that are tagged as neutral have certain polarity in other domains, thus confirming the notion that the use of lexicon is domaindependent [31]. SentiStrength is proposed by Thelwall et al [32], in which the polarity strength values range from 1 to 5 and makes use of emoticons, negations and boosting words for polarity detection.…”

Section: Lexicon-based Approachesmentioning

confidence: 99%

“…Furthermore, by investigating the tone of words within a text through sentiment analysis, it can shed some light on people's reactions towards a particular topic that could either be negative, neutral, or positive, thus this insight can help the decision-makers to understand public behavior and how to tackle the issues at hand [4]. Sentiment analysis has been successfully applied to study different domains, for instance news headlines [5], movie reviews [6], presidential elections [7], and GST [8].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Climate Change Sentiment Analysis Using Lexicon, Machine Learning and Hybrid Approaches

Sham

Mohamed

2022

Sustainability

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The emissions of greenhouse gases, such as carbon dioxide, into the biosphere have the consequence of warming up the planet, hence the existence of climate change. Sentiment analysis has been a popular subject and there has been a plethora of research conducted in this area in recent decades, typically on social media platforms such as Twitter, due to the proliferation of data generated today during discussions on climate change. However, there is not much research on the performances of different sentiment analysis approaches using lexicon, machine learning and hybrid methods, particularly within this domain-specific sentiment. This study aims to find the most effective sentiment analysis approach for climate change tweets and related domains by performing a comparative evaluation of various sentiment analysis approaches. In this context, seven lexicon-based approaches were used, namely SentiWordNet, TextBlob, VADER, SentiStrength, Hu and Liu, MPQA, and WKWSCI. Meanwhile, three machine learning classifiers were used, namely Support Vector Machine, Naïve Bayes, and Logistic Regression, by using two feature extraction techniques, which were Bag-of-Words and TF–IDF. Next, the hybridization between lexicon-based and machine learning-based approaches was performed. The results indicate that the hybrid method outperformed the other two approaches, with hybrid TextBlob and Logistic Regression achieving an F1-score of 75.3%; thus, this has been chosen as the most effective approach. This study also found that lemmatization improved the accuracy of machine learning and hybrid approaches by 1.6%. Meanwhile, the TF–IDF feature extraction technique was slightly better than BoW by increasing the accuracy of the Logistic Regression classifier by 0.6%. However, TF–IDF and BoW had an identical effect on SVM and NB. Future works will include investigating the suitability of deep learning approaches toward this domain-specific sentiment on social media platforms.

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Section: Lexicon-based Approachesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Climate Change Sentiment Analysis Using Lexicon, Machine Learning and Hybrid Approaches

Sham

Mohamed

2022

Sustainability

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“…POS tagging can help improve the accuracy of this method. Yadav et al [21] studied the importance of a domain-specific Lexicon for sentiment analysis. They built a domain-specific lexicon by introducing a bigram algorithm with a proposed strategy for developing some new corpora.…”

Section: Introductionmentioning

confidence: 99%

Lexicon-based sentiment analysis for Kannada-English code-switch text

Chundi

Hulipalled²,

Simha³

2023

IJ-AI

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Sentiment analysis is the process of computationally recognizing and classifying the attitudes conveyed in each text towards a particular topic, product, etc. which is either positive or negative. Sentiment analysis is one of the interesting applications of natural language processing (NLP) and which is used to analyze the social media. Text in social media is casual and it can be written either in code-switch or monolingual text. Several researchers have implemented sentiment analysis on monolingual text, though sentiments can be expressed in code-switch text. Sentiment analysis can be applied through deep learning (DL), machine learning (ML), or a Lexicon-based approach. Machine learning (ML) and deep learning (DL) methods are time-consuming, computationally expensive, and need training data for analysis. Lexicon-based method does not require training data and requires less time to find the sentiments in comparison with ML and DL. In this paper, we propose the Lexicon-based approach (NBLex) to analyze the sentiments expressed in Kannada-English code-switch text. This is the first effort that targets to perform sentiment analysis in Kannada-English code-switch text using the Lexicon-based approach. The proposed approach performed with better accuracy of 83.2% and 83% of F1-score.

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“…More recently, Chen et al [25] included a sentiment analysis score into a predictive model of crime, resulting in improvements in predictive capacity. This approach relies on a general use lexicon model, which may not reflect perceptions and polarities for particular domains, such as security [26]. In addition, this approach also focuses on the entire Twitter content, increasing the risk of including content non-related to security.…”

Section: Introductionmentioning

confidence: 99%

Quantifying Perception of Security Through Social Media and Its Relationship With Crime

et al. 2021

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The perception of security refers to the subjective evaluation of risks related to security events and the magnitude of their consequences. Negative perceptions have severe implications in society. These feelings are commonly quantified by citizen's surveys, which are time-consuming and do not adapt well to the changing dynamics linked to security. Recently, Twitter social network emerged as an alternative to quantify some of these feelings dynamically. However, most of these approaches focused on counting the amount of content related to crime, which is only one of the relevant factors determining the perception of security. In addition, these models do not account for the polarity and tone underlying the social network content. This work introduces a model for quantification of Perception of Security on Twitter based on sentiment analysis and studies its relationship with actual crime. The model relies on an automatic strategy for filtering content related to security based on a support vector machine classifier and quantifying the sentiment in the posts using a multinomial naive Bayes classifier. These measures of sentiment allow computing estimates of perception of security at a daily scale. This model studied more than 1.700.000 tweets in Bogotá (Colombia), collected during more than one year (March 18 of 2019 -April 28 of 2020). Results suggest that machine learning-based approaches may outperform previous security content filtering strategies by 21% in F1-score and provide quantifications on the sentiments of about 40% on accuracy. Trained models were used to provide daily estimates of perception of security, which are different from those captured by counting the number of posts related to security. Furthermore, the associations between perceptions and actual crimes exhibited monthly variations and showed that in some months, perceptions were more related to robbery, a crime with high incidence in the city. These results may help decisionmakers devise strategies to reduce the impact of the negative perception on citizens.

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Enhancing Sentiment Analysis Using Domain-Specific Lexicon: A Case Study on GST

Cited by 6 publications

References 7 publications

Climate Change Sentiment Analysis Using Lexicon, Machine Learning and Hybrid Approaches

Climate Change Sentiment Analysis Using Lexicon, Machine Learning and Hybrid Approaches

Lexicon-based sentiment analysis for Kannada-English code-switch text

Quantifying Perception of Security Through Social Media and Its Relationship With Crime

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