Sentiment Analysis Using Lexicon and Machine Learning-Based Approaches: A Survey

Verma, Binita; Thakur, Ramjeevan Singh

doi:10.1007/978-981-10-8198-9_46

Cited by 39 publications

(24 citation statements)

References 11 publications

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“…In the literature, this automatic extraction of opinions is known as opinion mining. A formal definition is that Opinion mining extracts and analyzes peoples' opinion about an entity [3]. Furthermore, it is also known as opinion finding, opinion extraction or opinion detection in the related literature.…”

Section: Introductionmentioning

confidence: 99%

OpinionML—Opinion Markup Language for Sentiment Representation

Attik¹,

Missen

Coustaty

et al. 2019

Symmetry

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It is the age of the social web, where people express themselves by giving their opinions about various issues, from their personal life to the world’s political issues. This process generates a lot of opinion data on the web that can be processed for valuable information, and therefore, semantic annotation of opinions becomes an important task. Unfortunately, existing opinion annotation schemes have failed to satisfy annotation challenges and cannot even adhere to the basic definition of opinion. Opinion holders, topical features and temporal expressions are major components of an opinion that remain ignored in existing annotation schemes. In this work, we propose OpinionML, a new Markup Language, that aims to compensate for the issues that existing typical opinion markup languages fail to resolve. We present a detailed discussion about existing annotation schemes and their associated problems. We argue that OpinionML is more robust, flexible and easier for annotating opinion data. Its modular approach while implementing a logical model provides us with a flexible and easier model of annotation. OpinionML can be considered a step towards “information symmetry”. It is an effort for consistent sentiment annotations across the research community. We perform experiments to prove robustness of the proposed OpinionML and the results demonstrate its capability of retrieving significant components of opinion segments. We also propose OpinionML ontology in an effort to make OpinionML more inter-operable. The ontology proposed is more complete than existing opinion ontologies like Marl and Onyx. A comprehensive comparison of the proposed ontology with existing sentiment ontologies Marl and Onyx proves its worth.

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Section: Introductionmentioning

confidence: 99%

OpinionML—Opinion Markup Language for Sentiment Representation

Attik¹,

Missen

Coustaty

et al. 2019

Symmetry

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“…Where aspect identification was based on a bag of nouns (BON). 3-Classification methods: The existing classification methods which are proposed in the literature are categorized into two groups: The machine learning approach, and the Lexicon-based approach [51], [52]. In the machine learning approach, Abdul aziz and Starkey [53] presented a method known as contextual analysis that can perform sentiment analysis without using any linguistics resources.…”

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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“…The development of machine learning has led to its gradual application to the analysis of textual sentiment [14]- [16]. Arunachalam et al [17] and Verma and Thakur [18] et al discussed common machine learning algorithms (such as Bayesian, LDA, and dynamic ontology classification) in text classification and emotional polarity information mining. Among them, the application of common machine learning algorithms in text sentiment analysis includes Bayesian [19], [20], LDA [21], [22], NB [23], SVM [24], [25], the maximum entropy method [26], and KNN [27].…”

Section: Related Workmentioning

confidence: 99%

“…, x i n−d+1 : n } in the i-th word vector dimension of the input matrix to produce the corresponding feature map. See formula (18).…”

Section: ) Vcpcnn-2d_samementioning

confidence: 99%

Variable Convolution and Pooling Convolutional Neural Network for Text Sentiment Classification

Dong

Tang

et al. 2020

IEEE Access

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With the popularity of the internet, the expression of emotions and methods of communication are becoming increasingly abundant, and most of these emotions are transmitted in text form. Text sentiment classification research mainly includes three methods based on sentiment dictionaries, machine learning and deep learning. In recent years, many deep learning-based works have used TextCNN (text convolution neural network) to extract text semantic information for text sentiment analysis. However, TextCNN only considers the length of the sentence when extracting semantic information. It ignores the semantic features between word vectors and only considers the maximum feature value of the feature image in the pooling layer without considering other information. Therefore, in this paper, we propose a convolutional neural network based on multiple convolutions and pooling for text sentiment classification (variable convolution and pooling convolution neural network, VCPCNN). There are three contributions in this paper. First, a multiconvolution and pooling neural network is proposed for the TextCNN network structure. Second, four convolution operations are introduced in the word embedding dimension or direction, which are helpful for mining the local features on the semantic dimensions of word vectors. Finally, average pooling is introduced in the pooling layer, which is beneficial for saving the important feature information of the extracted features. The verification test was carried out on four emotional datasets, including English emotional polarity, Chinese emotional polarity, Chinese subjective and objective emotion and Chinese multicategory. Our apporach is effective in that its result was up to 1.97% higher than that of the TextCNN network. INDEX TERMS Text sentiment classification, deep learning, CNN.

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Sentiment Analysis Using Lexicon and Machine Learning-Based Approaches: A Survey

Cited by 39 publications

References 11 publications

OpinionML—Opinion Markup Language for Sentiment Representation

OpinionML—Opinion Markup Language for Sentiment Representation

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

Variable Convolution and Pooling Convolutional Neural Network for Text Sentiment Classification

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