Comprehensive Study on Lexicon-based Ensemble Classification Sentiment Analysis

Augustyniak, Łukasz; Szymański, Piotr; Kajdanowicz, Tomasz; Tuliglowicz, Wlodzimierz

doi:10.3390/e18010004

Cited by 33 publications

(23 citation statements)

References 28 publications

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“…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]. Liu's lexicon [67] is a fully established example of this approach.…”

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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“…Firmino Alves et al (2013) give an insight of the main approaches for classifying sentiment polarity which are: machine learning, statistical approach, semantic approach and approach based on lexical analysis or thesaurus. Augustyniak, Łukasz et al (2015) describe that in the world of opinion mining predicting sentiment polarity from the text can be done by employing the specialists to manually classify the polarity, and can be done automatically or using both techniques. Hemalatha et al (2012) shows a very nice approach for pre-processing Twitter data following simple steps, and demonstrates how to prepare the data for training in machine learning technique.…”

Section: Sentiment Analysis On Twitter Datamentioning

confidence: 99%

“…Bandgar and Kumar (2015) using their research methodology illustrated how to create a windows application for real-time Twitter data for pre-processing of text data using available natural language processing resources like WordNet, SMS dictionary, Stanford dictionary. Augustyniak, Łukasz, et al (2015) proposed a new method called "frequentiment" that robotically evaluates sentiments (opinions) of the user from amazon reviews data set.…”

Section: Sentiment Analysis On Twitter Datamentioning

confidence: 99%

Sentiment Analysis on twitter data using Machine Learning

2020

jxu

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In the world of social media people are more responsive towards product or certain events that are currently occurring. This response given by the user is in form of raw textual data (Semi Structured Data) in different languages and terms, which contains noise in data as well as critical information that encourage the analyst to discover knowledge and pattern from the dataset available. This is useful for decision making and taking strategic decision for the future market.To discover this unknown information from the linguistic data Natural Language Processing (NLP) and Data Mining techniques are most focused research terms used for sentiment analysis. In the derived approach the analysis on Twitter data to detect sentiment of the people throughout the world using machine learning techniques. Here the data set available for research is from Twitter for world cup Soccer 2014, held in Brazil. During this period, many people had given their opinion, emotion and attitude about the game, promotion, players. By filtering and analyzing the data using natural language processing techniques, and sentiment polarity has been calculated based on the emotion word detected in the user tweets. The data set is normalized to be used by machine learning algorithm and prepared using natural language processing techniques like Word Tokenization, Stemming and lemmatization, POS (Part of speech) Tagger, NER (Name Entity recognition) and parser to extract emotions for the textual data from each tweet. This approach is iv implemented using Python programming language and Natural Language Toolkit (NLTK), which is openly available for academic as well as for research purpose. Derived algorithm extracts emotional words using WordNet with its POS (Part-of-Speech) for the word in a sentence that has a meaning in current context, and is assigned sentiment polarity using 'SentWordNet' Dictionary or using lexicon based method. The resultant polarity assigned is further analyzed using Naïve Bayes and SVM (support vector Machine) machine learning algorithm and visualized data on WEKA platform. Finally, the goal is to compare both the results of implementation and prove the best approach for sentiment analysis on social media for semi structured data.

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“…It has been found that the accuracy achieved from such lexicons outperforms other LB approaches. Augusttniak et al (2016) proposed a novel method frequentiment to count the sentiment orientation. The method was used to generate unigram, bigram and trigram lexicons.…”

Section: Lb Approachmentioning

confidence: 99%

An innovative and efficient method for Twitter sentiment analysis

Suresh

2019

IJDMMM

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The research in sentiment analysis is one of the most accomplished fields in data mining area. Specifically, sentiment analysis centres on analysing attitudes and opinions relating a particular topic of interest using machine learning approaches, lexicon-based approaches or hybrid approaches. Users are purposive to develop an automated system that could identify and classify sentiments in the related text. An efficient approach for predicting sentiments would allow us to bring out opinions from the web contents and to predict online public choices, which could prove valuable for ameliorating changes in the sentiment of Twitter users. This paper presents a proposed model to analyse the brand impact using the real data gathered from the micro blog, Twitter collected over a period of 14 months and also discusses the review covering the existing methods and approaches in sentiment analysis. Twitter-based information gathering techniques enable collecting direct responses from the target audience; it provides valuable understanding into public sentiments in the prediction of an opinion of a particular product. The experimental result shows that the proposed method for Twitter sentiment analysis is the best, with an unrivalled accuracy of 86.8%.

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Comprehensive Study on Lexicon-based Ensemble Classification Sentiment Analysis

Cited by 33 publications

References 28 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

Sentiment Analysis on twitter data using Machine Learning

An innovative and efficient method for Twitter sentiment analysis

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