Context Enrichment Model Based Framework for Sentiment Analysis

Yusof, Nor Nadiah; Mohamed, Azlinah; Abdul-Rahman, Shuzlina

doi:10.1007/978-981-15-0399-3_26

Cited by 5 publications

(4 citation statements)

References 33 publications

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“…The computational analysis of text to determine people's opinions, appraisals, attitudes, emotions, and sentiment polarity (positive, negative, or neutral) towards entities, situations, events, and topics is known as sentiment analysis or opinion mining [7] [18]. It is an excellent tool for businesses to analyse opinions expressed by users on social media without explicitly asking any questions, as this approach often reflects their genuine thoughts [18] [19].…”

Section: Sentiment Analysismentioning

confidence: 99%

Sentiment classification from reviews for tourism analytics

Haris

Mutalib

Malik

et al. 2023

Int. J. Adv. Intell. Informatics

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User-generated content is critical for tourism destination management as it could help them identify their customers' opinions and come up with solutions to upgrade their tourism organizations as it could help them identify customer opinions. There are many reviews on social media and it is difficult for these organizations to analyse the reviews manually. By applying sentiment classification, reviews can be classified into several classes and help ease decision-making. The reviews contain noisy contents, such as typos and emoticons, which could affect the accuracy of the classifiers. This study evaluates the reviews using Support Vector Machine and Random Forest models to identify a suitable classifier. The main phases in this study are data collection, data preparation, data labelling and modelling phases. The reviews are labelled into three sentiments; positive, neutral, and negative. During pre-processing, steps such as removing the missing value, tokenization, case folding, stop words removal, stemming, and applying n-grams are performed. The result of this research is evaluated by looking at the performance of the models based on accuracy where the result with the highest accuracy is chosen as the solution. In this study, data is data from TripAdvisor and Google reviews using web scraping tools. The findings show that the Support Vector Machine model with 5-fold cross-validation the most suitable classifier with an accuracy of 67.97% compared to Naive Bayes with 61.33% accuracy and Random Forest classifier with 63.55% accuracy. In conclusion, the result of this paper could provide important information in tourism besides determining the suitable algorithm to be used for Sentiment Analysis related to the tourism domain.

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Section: Sentiment Analysismentioning

confidence: 99%

Sentiment classification from reviews for tourism analytics

Haris

Mutalib

Malik

et al. 2023

Int. J. Adv. Intell. Informatics

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“…There are not many solutions focusing on context-based Sentiment Analysis models. A context enrichment model for Sentiment Analysis is proposed in [4]. The authors add several processing steps, prior to sentiment classification, in order to augment the dataset with context.…”

Section: Related Workmentioning

confidence: 99%

“…Opinion Mining and Sentiment Analysis are related research topics, at the intersection of Machine Learning and Natural Language Processing, that, recently, have been studied intensively [1][2][3][4][5][6]. The interest in these related topics is due to the wide range of applications where they can be used (e.g., advertising, politics, business, etc.)…”

Section: Introductionmentioning

confidence: 99%

Topic-Based Document-Level Sentiment Analysis Using Contextual Cues

et al. 2021

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Document-level Sentiment Analysis is a complex task that implies the analysis of large textual content that can incorporate multiple contradictory polarities at the phrase and word levels. Most of the current approaches either represent textual data using pre-trained word embeddings without considering the local context that can be extracted from the dataset, or they detect the overall topic polarity without considering both the local and global context. In this paper, we propose a novel document-topic embedding model, DocTopic2Vec, for document-level polarity detection in large texts by employing general and specific contextual cues obtained through the use of document embeddings (Doc2Vec) and Topic Modeling. In our approach, (1) we use a large dataset with game reviews to create different word embeddings by applying Word2Vec, FastText, and GloVe, (2) we create Doc2Vecs enriched with the local context given by the word embeddings for each review, (3) we construct topic embeddings Topic2Vec using three Topic Modeling algorithms, i.e., LDA, NMF, and LSI, to enhance the global context of the Sentiment Analysis task, (4) for each document and its dominant topic, we build the new DocTopic2Vec by concatenating the Doc2Vec with the Topic2Vec created with the same word embedding. We also design six new Convolutional-based (Bidirectional) Recurrent Deep Neural Network Architectures that show promising results for this task. The proposed DocTopic2Vecs are used to benchmark multiple Machine and Deep Learning models, i.e., a Logistic Regression model, used as a baseline, and 18 Deep Neural Networks Architectures. The experimental results show that the new embedding and the new Deep Neural Network Architectures achieve better results than the baseline, i.e., Logistic Regression and Doc2Vec.

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“…Recently, a significant amount of research has been conducted on opinion mining and sentiment analysis by applying machine learning and deep learning in various domains [4][5][6]. Opinion and sentiment analysis activities have been improved with the application of several neural networks, such as convolutional neural networks (CNNs), gated recurrent unit (GRU) or long short-term memory (LSTM), and recurrent neural networks (RNNs) [7].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Legal Sentiment Analysis: A Convolutional Neural Network–Long Short-Term Memory Document-Level Model

Abimbola,

de La Cal Marin,

Tan

2024

MAKE

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This research investigates the application of deep learning in sentiment analysis of Canadian maritime case law. It offers a framework for improving maritime law and legal analytic policy-making procedures. The automation of legal document extraction takes center stage, underscoring the vital role sentiment analysis plays at the document level. Therefore, this study introduces a novel strategy for sentiment analysis in Canadian maritime case law, combining sentiment case law approaches with state-of-the-art deep learning techniques. The overarching goal is to systematically unearth hidden biases within case law and investigate their impact on legal outcomes. Employing Convolutional Neural Network (CNN)- and long short-term memory (LSTM)-based models, this research achieves a remarkable accuracy of 98.05% for categorizing instances. In contrast, conventional machine learning techniques such as support vector machine (SVM) yield an accuracy rate of 52.57%, naïve Bayes at 57.44%, and logistic regression at 61.86%. The superior accuracy of the CNN and STM model combination underscores its usefulness in legal sentiment analysis, offering promising future applications in diverse fields like legal analytics and policy design. These findings mark a significant choice for AI-powered legal tools, presenting more sophisticated and sentiment-aware options for the legal profession.

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Context Enrichment Model Based Framework for Sentiment Analysis

Cited by 5 publications

References 33 publications

Sentiment classification from reviews for tourism analytics

Sentiment classification from reviews for tourism analytics

Topic-Based Document-Level Sentiment Analysis Using Contextual Cues

Enhancing Legal Sentiment Analysis: A Convolutional Neural Network–Long Short-Term Memory Document-Level Model

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