Sentiment Analysis of News Articles: A Lexicon based Approach

Taj, S. Meher; Shaikh, Baby Bakhtawer; Meghji, Areej Fatemah

doi:10.1109/icomet.2019.8673428

Cited by 88 publications

(31 citation statements)

References 14 publications

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“…The classification applied in this way is based on naïve Bayes classifier. In the lexicon-based approach, one of the categories of lexicons approach is based on dictionary-based [62]- [65]. A set of hand-picked sentiment selected (features) are created and then expanded using a thesaurus or tools like WordNet [66].…”

Section: Sentiment Analysismentioning

confidence: 99%

Sentiment Analysis Technique and Neutrosophic Set Theory for Mining and Ranking Big Data From Online Reviews

2021

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Recently, a huge amount of online consumer reviews (OCRs) is being generated through social media, web contents, and microblogs. This scale of big data cannot be handled by traditional methods. Sentiment analysis (SA) or opinion mining is emerging as a powerful and efficient tool in big data analytics and improving decision making. This research paper introduces a novel method that integrates neutrosophic set (NS) theory into the SA technique and multi-attribute decision making (MADM) to rank the different products based on numerous online reviews. The method consists of two parts: Determining sentiment scores of the online reviews based on the SA technique and ranking alternative products via NS theory. In the first part, the online reviews of the alternative products concerning multiple features are crawled and pre-processed. A neutral lexicon consists of 228 neutral words and phrases is compiled and the Valence Aware Dictionary and sEntiment Reasoner (VADER) for sentiment reasoning is adapted to handle the neutral data. The compiled neutral lexicon, as well as the adapted VADER, are utilized to build a novel adaptation called Neutro-VADER. The Neutro-VADER assigns positive, neutral, and negative sentiment scores to each review concerning the product feature. In this stage, the novel idea is to point out the positive, neutral, and negative sentiment scores as the truth, indeterminacy, and falsity memberships degrees of the neutrosophic number. The overall performance of each alternative concerning each feature based on a neutrosophic number is measured. In the second part, the ranking of alternatives is being evaluated through the simplified neutrosophic number weighted averaging (SNNWA) operator and cosine similarity measure methods. A case study with real datasets (Twitter datasets) is provided to illustrate the application of the proposed method. The results show good performance in handling the neutral data on the SA stage as well as the ranking stage. In the SA stage, findings show that the Neutro-VADER in the proposed method can deal successfully with all types of uncertainties including indeterminacy comparable with the traditional VADER in the other methods. In the ranking stage, the results show a great similarity and consistency while using other ranking methods such as PROMETHEE II, TOPSIS, and TODIM methods.

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Section: Sentiment Analysismentioning

confidence: 99%

Sentiment Analysis Technique and Neutrosophic Set Theory for Mining and Ranking Big Data From Online Reviews

2021

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“…The family of dictionary-based methods [45,46,47,52,53,57], which have become immensely popular in recent years, are based on utilizing lexicon resources such as SentiWordNet [53], WordNet-Affect [58] which are built using WordNet [44] lexical information. They have been developed to annotate their synsets with sentiment scores (positively, negatively, and objectively) ranged from 0 to 1 for each sentimental class.…”

Section: Related Workmentioning

confidence: 99%

Experimental Results on Customer Reviews Using Lexicon-Based Word Polarity Identification Method

Abdalgader

Shibli

2020

IEEE Access

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Determining the polarities of words in a given context has been in existence since the inception of computational linguistics, text mining, and sentiment analysis. Due to its fundamental role in determining the overall semantic orientation of natural language expressions, it is considered one of the most challenging issues facing these areas of research. This paper introduces a new implementation of the lexicon-based word polarity identification method on several customer reviews datasets. Herein, we use a variation of a lexiconbased word polarity identification method that operates by computing the semantic relatedness between the context expansion set of the target word and a synonym expansion set comprising the synonyms of all words surrounding the target word within the original text fragment. The polarity of the target word is determined as that for which the semantic relatedness between these two meaningful sets is the highest. Unlike most existing lexicon-based multi-polarity word identification methods, the used method is not based on estimating pairwise relatedness at term-level, but instead, it is based on measuring semantic relatedness at the fragmentlevel. This enables the exploration and capture of a higher degree of semantic and sentimental information, and is more consistent with people' understanding through the consideration of the larger context in which the word appears. Its performance can be further improved by incorporating an initial step in which the relative negation scope of words in the given text fragment is managed while determining their sentiment orientation. The implementation results demonstrate that the used variation of the lexicon-based word polarity identification method performs favourably against compared methods, as evaluated on numerous benchmark datasets through stand-alone and end-to-end evaluation models.

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“…Then, all the data was converted to lowercase form and stemming was performed. In the final section technology out of 401 articles, 136 articles were positive and 24 articles were negative while the rest others were neutral [3] In this research the author proposes to use lexicon based approach to find the sentimental analysis of the news articles. BBC news articles of five different categories such as sports, business, technology, politics and entertainment were used as data set.…”

Section: Related Workmentioning

confidence: 99%

News Article to Uncover Media Bias

2020

IJATCSE

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Media bias towards political issues is on the rise in the Indian subcontinent and South Asian nations. Media plays an extremely important role in the functioning of any democratic system. A bias media can lead to the formation of fallacious perceptions among the public. In recent years, there have been numerous instances where the ruling parties allegedly used media to manipulate people's opinions in their favor. In this paper aims to study the news articles of major media houses, over the past few months and to find the degree of political bias. The data will be scraped from the published articles by various media houses and people's reactions to it from social media. The concept of clustering and topic modelling will be used to obtain the articles related to relevant topics from the data acquired. Finally, various classification techniques Naïve Bayes' classifier, Bernoulli Bayes classifier, MNB classifier, stochastic gradient descent, NuSVC and LinearSVC will be used to determine the positive or negative sentiment for a particular topic. The result will draw a comparison between the media reaction and public sentiments towards a certain political party or issue. It will help us in determining the most trustworthy media houses in the country. It will also serve as a tool to put a check on the prejudiced news shown to the public and protect them from being manipulated.

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Sentiment Analysis of News Articles: A Lexicon based Approach

Cited by 88 publications

References 14 publications

Sentiment Analysis Technique and Neutrosophic Set Theory for Mining and Ranking Big Data From Online Reviews

Sentiment Analysis Technique and Neutrosophic Set Theory for Mining and Ranking Big Data From Online Reviews

Experimental Results on Customer Reviews Using Lexicon-Based Word Polarity Identification Method

News Article to Uncover Media Bias

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