Unsupervised Learning-Based Sentiment Analysis with Reviewer’s Emotion

Patel, Himanshu K.; Verma, J. P.; Patel, Atul

doi:10.1007/978-981-15-7804-5_6

Cited by 4 publications

(3 citation statements)

References 17 publications

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“…It is the simplest similarity measure, which calculates similarity based on the longest common subsequence between generated summary and referenced summary. Equation (16) gives the basic formula of LCS measure.…”

Section: Longest Common Subsequencementioning

confidence: 99%

GETS : Sentence Scoring Scheme in Graph-based Extractive Text Summarization for Text Mining Applications

Bhattacharya

Verma

Shir

et al. 2023

Preprint

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Recently, there is an exponential influx of textual data in big data applications, which necessitates the requirement of text mining tools for analysis of data. In Text Mining applications (TM), Text Summarization (TS) has emerged as an emergent field in Natural Language Processing (NLP). Mostly, in TS, abstractive approaches are presented which build complex models, and thus, a shift is envisioned towards graph-based extractive text summarization models. Such models allow review and feedback analysis of a service or product, and have the benefits of being less complex, flexible, and require low computational resources. This makes them an effective fit for modern text mining based big data and Internet-of-Things (IoT) applications. Thus, in the proposed work, we present a scheme, GETS, which exploits a graph-based model to establish relations between words and sentences based on statistical operations. In the scheme, a post processing phase is presented which uses sentence clustering based on graph preparation. To make the scheme scalable fit for real world applications, we use the Apache Spark environment for parallel execution of graph-based operations. In experimental setup, the Recall-oriented Understudying Gisting Evaluation (ROUGE) parameters is used to evaluate the proposed graph based model with a comparative analysis with ROUGE 1,2,L measures. Comparative analysis is done based on clustered and non-clustered approaches. The obtained results renders the scheme effective as a backend of Artificial Intelligence (AI) models in crowdsourcing applications and decision-analytics models.

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Section: Longest Common Subsequencementioning

confidence: 99%

GETS : Sentence Scoring Scheme in Graph-based Extractive Text Summarization for Text Mining Applications

Bhattacharya

Verma

Shir

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Based on its comparison method, it has various variants, for example, the ROUGE-N comparison of N-gram, the ROUGE-L comparison of the longest subsequence, and ROUGE-S, which uses the skip-gram model. The basic formula of ROUGE-N is presented in Equation (20).…”

Section: Rouge [78]mentioning

confidence: 99%

“…It is useful in cases where the quality of information is more important and thus accurate summary generation is highly required. Further, the classifications can be both supervised and unsupervised for the extraction of relationships from the main text document [19][20][21]. The primary goal of this study is to implement extractive text summarization (ETS) in a big data analytics environment.…”

Section: Introductionmentioning

confidence: 99%

Graph-Based Extractive Text Summarization Sentence Scoring Scheme for Big Data Applications

Verma,

Bhargav,

Bhavsar

et al. 2023

Information

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The recent advancements in big data and natural language processing (NLP) have necessitated proficient text mining (TM) schemes that can interpret and analyze voluminous textual data. Text summarization (TS) acts as an essential pillar within recommendation engines. Despite the prevalent use of abstractive techniques in TS, an anticipated shift towards a graph-based extractive TS (ETS) scheme is becoming apparent. The models, although simpler and less resource-intensive, are key in assessing reviews and feedback on products or services. Nonetheless, current methodologies have not fully resolved concerns surrounding complexity, adaptability, and computational demands. Thus, we propose our scheme, GETS, utilizing a graph-based model to forge connections among words and sentences through statistical procedures. The structure encompasses a post-processing stage that includes graph-based sentence clustering. Employing the Apache Spark framework, the scheme is designed for parallel execution, making it adaptable to real-world applications. For evaluation, we selected 500 documents from the WikiHow and Opinosis datasets, categorized them into five classes, and applied the recall-oriented understudying gisting evaluation (ROUGE) parameters for comparison with measures ROUGE-1, 2, and L. The results include recall scores of 0.3942, 0.0952, and 0.3436 for ROUGE-1, 2, and L, respectively (when using the clustered approach). Through a juxtaposition with existing models such as BERTEXT (with 3-gram, 4-gram) and MATCHSUM, our scheme has demonstrated notable improvements, substantiating its applicability and effectiveness in real-world scenarios.

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Tasks, Approaches, and Avenues of Opinion Mining, Sentiment Analysis, and Emotion Analysis

Idrees

Eldin

Mohsen

et al. 2022

Research Anthology on Implementing Sentiment Analysis Across Multiple Disciplines

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Every successful business aims to know how customers feel about its brands, services, and products. People freely express their views, ideas, sentiments, and opinions on social media for their day-to-day activities, for product reviews, for surveys, and even for their public opinions. This process provides a fortune of valuable resources about the market for any type of business. Unfortunately, it's impossible to manually analyze this massive quantity of information. Sentiment analysis (SA) and opinion mining (OM), as new fields of natural language processing, have the potential benefit of analyzing such a huge amount of data. SA or OM is the computational treatment of opinions, sentiments, and subjectivity of text. This chapter introduces the reader to a survey of different text SA and OM proposed techniques and approaches. The authors discuss in detail various approaches to perform a computational treatment for sentiments and opinions with their strengths and drawbacks.

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Unsupervised Learning-Based Sentiment Analysis with Reviewer’s Emotion

Cited by 4 publications

References 17 publications

GETS : Sentence Scoring Scheme in Graph-based Extractive Text Summarization for Text Mining Applications

GETS : Sentence Scoring Scheme in Graph-based Extractive Text Summarization for Text Mining Applications

Graph-Based Extractive Text Summarization Sentence Scoring Scheme for Big Data Applications

Tasks, Approaches, and Avenues of Opinion Mining, Sentiment Analysis, and Emotion Analysis

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