An Opinion Mining Approach to Handle Perspectivism and Ambiguity: Moving Toward Neutrosophic Logic

Essameldin, Reem; Ismail, Ahmed Rageh; Darwish, Saad M.

doi:10.1109/access.2022.3183108

Cited by 6 publications

(13 citation statements)

References 40 publications

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“…This enhancement will be achieved by improving the sentiment analysis results, specifically designed to handle the uncertain and ambiguous data gathered from Twitter, about various companies and people's viewpoints regarding them. To address this challenge, we employ neutrosophic logic (NL) [12]. Furthermore, the output of sentiment analysis, alongside historical stock market data collected from Yahoo Finance, is incorporated into a model known as long short-term memory (LSTM) with the utmost accuracy to forecast the stock market movement.…”

Section: Contributionmentioning

confidence: 99%

“…Reem et al [12] introduced an opinion-mining model tailored for social media to tackle the challenges associated with ambiguous opinion classification. The proposed model incorporates social network analysis utilizing the UCINET tool, which is used for analyzing social network data (the University of California at Irvine Network), neural networks to assess the influence levels of users, and a classifier to combine their influence with the polarity of their texts.…”

Section: Related Workmentioning

confidence: 99%

“…The purpose of this phase is achieved through the following steps: type 1 or type 2, introduces an overlapping region between classes, leading to ambiguity in the classification. Conversely, NL processing is adept at handling such ambiguity, thereby enhancing the efficiency of the classification process, as illustrated in Figure 2 [12,29,32,33]. Our aim is to enhance the efficiency of classification by using NL with a sentiment lexicon.…”

Section: Sentiment Analysis Based On Neutrosophic Logicmentioning

confidence: 99%

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Enhancing the Prediction of Stock Market Movement Using Neutrosophic-Logic-Based Sentiment Analysis

Abdelfattah,

Darwish,

Elkaffas

2024

JTAER

Self Cite

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Social media platforms have allowed many people to publicly express and disseminate their opinions. A topic of considerable interest among researchers is the impact of social media on predicting the stock market. Positive or negative feedback about a company or service can potentially impact its stock price. Nevertheless, the prediction of stock market movement using sentiment analysis (SA) encounters hurdles stemming from the imprecisions observed in SA techniques demonstrated in prior studies, which overlook the uncertainty inherent in the data and consequently directly undermine the credibility of stock market indicators. In this paper, we proposed a novel model to enhance the prediction of stock market movements using SA by improving the process of SA using neutrosophic logic (NL), which accurately classifies tweets by handling uncertain and indeterminate data. For the prediction model, we use the result of sentiment analysis and historical stock market data as input for a deep learning algorithm called long short-term memory (LSTM) to predict the stock movement after a specific number of days. The results of this study demonstrated a predictive accuracy that surpasses the accuracy rate of previous studies in predicting stock price fluctuations when using the same dataset.

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Section: Contributionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Sentiment Analysis Based On Neutrosophic Logicmentioning

confidence: 99%

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Enhancing the Prediction of Stock Market Movement Using Neutrosophic-Logic-Based Sentiment Analysis

Abdelfattah,

Darwish,

Elkaffas

2024

JTAER

Self Cite

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“…PageRank mainly focuses on the outgoing links from web pages. Equation (1) gives the simplified version of PageRank.…”

Section: Pagerankmentioning

confidence: 99%

“…The modern Internet communication has shifted towards micorblogging, community formation (on Twitter, Facebook, and other sites), and opinion based polling. Most of the generated information falls under the wider umbrella of Text Mining (TM) approaches, where the data, or information is gathered from devices, and is distributed to different users for consumption 1 . Another field is crowdsourcing 2 , which includes involvement of public opinions, work distribution, and community shares from a large group of persons.…”

Section: Introductionmentioning

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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Emotion quantification and classification using the neutrosophic approach to deep learning

Sharma,

Kandasamy,

Vasantha

2023

Applied Soft Computing

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An Opinion Mining Approach to Handle Perspectivism and Ambiguity: Moving Toward Neutrosophic Logic

Cited by 6 publications

References 40 publications

Enhancing the Prediction of Stock Market Movement Using Neutrosophic-Logic-Based Sentiment Analysis

Enhancing the Prediction of Stock Market Movement Using Neutrosophic-Logic-Based Sentiment Analysis

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

Emotion quantification and classification using the neutrosophic approach to deep learning

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