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The purpose of this paper is to initially perform Senti-WordNet (SWN)- and point wise mutual information (PMI)-based polarity computation and based polarity updation. When the SWN polarity and polarity mismatched, the vote flipping algorithm (VFA) is employed.

Recently, in domains like social media(SM), healthcare, hotel, car, product data, etc., research on sentiment analysis (SA) has massively increased. In addition, there is no approach for analyzing the positive or negative orientations of every single aspect in a document (a tweet, a review, as well as a piece of news, among others). For SA as well as polarity classification, several researchers have used SWN as a lexical resource. Nevertheless, these lexicons show lower-level performance for sentiment classification (SC) than domain-specific lexicons (DSL). Likewise, in some scenarios, the same term is utilized differently between domain and general knowledge lexicons. While concerning different domains, most words have one sentiment class in SWN, and in the annotated data set, their occurrence signifies a strong inclination with the other sentiment class. Hence, this paper chiefly concentrates on the drawbacks of adapting domain-dependent sentiment lexicon (DDSL) from a collection of labeled user reviews and domain-independent lexicon (DIL) for proposing a framework centered on the information theory that could predict the correct polarity of the words (positive, neutral and negative). The proposed work initially performs SWN- and PMI-based polarity computation and based polarity updation. When the SWN polarity and polarity mismatched, the vote flipping algorithm (VFA) is employed. Finally, the predicted polarity is inputted to the mtf-idf-based SVM-NN classifier for the SC of reviews. The outcomes are examined and contrasted to the other existing techniques to verify that the proposed work has predicted the class of the reviews more effectually for different datasets.

There is no approach for analyzing the positive or negative orientations of every single aspect in a document (a tweet, a review, as well as a piece of news, among others). For SA as well as polarity classification, several researchers have used SWN as a lexical resource. Nevertheless, these lexicons show lower-level performance for sentiment classification (SC) than domain-specific lexicons (DSL). Likewise, in some scenarios, the same term is utilized differently between domain and general knowledge lexicons. While concerning different domains, most words have one sentiment class in SWN, and in the annotated data set their occurrence signifies a strong inclination with the other sentiment class.

The proposed work initially performs SWN- and PMI-based polarity computation, and based polarity updation. When the SWN polarity and polarity mismatched, the vote flipping algorithm (VFA) is employed.

Digital resources such as smart applications reviews and online feedback information are important sources to seek customers’ feedback and input. This paper aims to help government entities gain insights on the needs and expectations of their customers. Towards this end, we propose an aspect-based sentiment analysis hybrid approach that integrates domain lexicons and rules to analyse the entities smart apps reviews. The proposed model aims to extract the important aspects from the reviews and classify the corresponding sentiments. This approach adopts language processing techniques, rules, and lexicons to address several sentiment analysis challenges, and produce summarized results. According to the reported results, the aspect extraction accuracy improves significantly when the implicit aspects are considered. Also, the integrated classification model outperforms the lexicon-based baseline and the other rules combinations by 5% in terms of Accuracy on average. Also, when using the same dataset, the proposed approach outperforms machine learning approaches that uses support vector machine (SVM). However, using these lexicons and rules as input features to the SVM model has achieved higher accuracy than other SVM models.

Aspect based sentiment classification is valuable for providing deeper insight into online consumer reviews (OCR). However, the majority of the previous studies explicitly determine the orientation of aspect related sentiment bearing word and overlook the aspect-context. Therefore, this paper aims to propose an aspect-context aware sentiment classification of OCR for deeper and more accurate insights.

In the proposed methodology, first, aspect descriptions and sentiment bearing words are extracted. Then, the skip-gram model is used to extract the first set of features to capture contextual information. For the second category of features, cosine similarity is used between a pre-defined seed word list and aspects, to capture aspect context sensitive sentiments. The third set of features includes weighted word vectors using term frequency-inverse document frequency. After concatenating features, ensemble classifier is used using three base classifiers.

Experimental results on two real-world data sets with variable lengths, acquired from Amazon.com and TripAdvisor.com, show that the advised ensemble approach significantly outperforms sentiment classification accuracy of state-of-the-art and baseline methods.

This method is capable of capturing the correct sentiment of ambiguous words and other special words by extracting aspect-context using word vector similarity instead of expensive lexical resources, and hence, shows superior performance in terms of accuracy as compared to other methods.

Multi-label Text Classification (MTC) is one of the most recent research trends in data mining and information retrieval domains because of many reasons such as the rapid growth of online data and the increasing tendency of internet users to be more comfortable with assigning multiple labels/tags to describe documents, emails, posts, etc. The dimensionality of labels makes MTC more difficult and challenging compared with traditional single-labeled text classification (TC). Because it is a natural extension of TC, several ways are proposed to benefit from the rich literature of TC through what is called problem transformation (PT) methods. Basically, PT methods transform the multi-label data into a single-label one that is suitable for traditional single-label classification algorithms. Another approach is to design novel classification algorithms customized for MTC. Over the past decade, several works have appeared on both approaches focusing mainly on the English language. This work aims to present an elaborate study of MTC of Arabic articles.

This paper presents a novel lexicon-based method for MTC, where the keywords that are most associated with each label are extracted from the training data along with a threshold that can later be used to determine whether each test document belongs to a certain label.

The experiments show that the presented approach outperforms the currently available approaches. Specifically, the results of our experiments show that the best accuracy obtained from existing approaches is only 18 per cent, whereas the accuracy of the presented lexicon-based approach can reach an accuracy level of 31 per cent.

Although there exist some tools that can be customized to address the MTC problem for Arabic text, their accuracies are very low when applied to Arabic articles. This paper presents a novel method for MTC. The experiments show that the presented approach outperforms the currently available approaches.

Vast volumes of rich online consumer-generated content (CGC) can be used effectively to gain important insights for decision-making, product improvement and brand management. Recently, many studies have proposed semi-supervised aspect-based sentiment classification of unstructured CGC. However, most of the existing CGC mining methods rely on explicitly detecting aspect-based sentiments and overlooking the context of sentiment-bearing words. Therefore, this study aims to extract implicit context-sensitive sentiment, and handle slangs, ambiguous, informal and special words used in CGC.

A novel text mining framework is proposed to detect and evaluate implicit semantic word relations and context. First, POS (part of speech) tagging is used for detecting aspect descriptions and sentiment-bearing words. Then, LDA (latent Dirichlet allocation) is used to group similar aspects together and to form an attribute. Semantically and contextually similar words are found using the skip-gram model for distributed word vectorisation. Finally, to find context-sensitive sentiment of each attribute, cosine similarity is used along with a set of positive and negative seed words.

Experimental results using more than 400,000 Amazon mobile phone reviews showed that the proposed method efficiently found product attributes and corresponding context-aware sentiments. This method also outperforms the classification accuracy of the baseline model and state-of-the-art techniques using context-sensitive information on data sets from two different domains.

Extracted attributes can be easily classified into consumer issues and brand merits. A brand-based comparative study is presented to demonstrate the practical significance of the proposed approach.

This paper presents a novel method for context-sensitive attribute-based sentiment analysis of CGC, which is useful for both brand and product improvement.

With the development of the internet, huge numbers of reviews are generated, disseminated, and shared on e-commerce and social media websites by internet users. These reviews usually indicate users’ opinions about products or services directly, and are thus valuable for efficient marketing. The purpose of this paper is to mine online users’ attitudes from a huge pool of reviews via automatic question answering.

The authors make use of online reviews to complete an online investigation via automatic question answering (AQA). In the process of AQA, question generation and extraction of corresponding answers are conducted via sentiment computing. In order to verify the performance of AQA for online investigation, online reviews from a well-known travel website, namely Tuniu.com, are used as the experimental data set. Finally, the experimental results from AQA vs a traditional questionnaire are compared.

The experimental results show that results between the AQA-based automatic questionnaire and the traditional questionnaire are consistent. Hence, the AQA method is reliable in identifying users’ attitudes. Although this paper takes Chinese tourism reviews as the experimental data, the method is domain and language independent.

To the best of the authors’ knowledge, this is the first study to use the AQA method to mine users’ attitudes towards tourism services. Using online reviews may overcome problems with using traditional questionnaires, such as high costs and long cycle for questionnaire design and answering.

The study aims to provide a critical review of the origin, development and process of sentiment analysis (SA) and a demonstration for hospitality researchers and students on how to perform SA using a sample study.

A critical review and sample case demonstration approach was applied. The sample study used Leximancer to perform SA using TripAdvisor review data.

A critical evaluation of the most popular SA tools was provided, highlighting their advantages and disadvantages. A step-by-step demonstration with data provided makes it possible for readers to learn this technique at own pace.

By providing a critical review of SA supported with a demonstration case study, this study makes a timely contribution for broader awareness and understanding, as well as the application of SA in hospitality.

The purpose of this paper is to examine and compare the in situ place experiences of people in Luton and Darlington.

The study used 109,998 geotagged tweets from Luton and Darlington between 2020 and 2022 and conducted topic modelling using latent Dirichlet allocation. Lexicons were created using GPT-4 to evaluate the eight dimensions of place experience for each topic.

The study found that Darlington had higher counts in the sensorial, behavioural, designed and mundane dimensions of place experience than Luton. Conversely, Luton had a higher prevalence of the affective and intellectual dimensions, attributed to political and faith-related tweets.

The study introduces a novel approach that uses AI-generated lexicons for place experience. These lexicons cover four facets, two intentions and two intensities of place experience, enabling detection of words from any domain. This approach can be useful not only for town and destination brand managers but also for researchers in any field.

Due to the large and fast growing sentiment analysis (SA) area recently, many new concepts and different nomenclatures have emerged without the desired organization. This confusion in the research field makes the understandability of the concepts hard and also hampers the comparison of different approaches. Thus, this paper aims to propose a hierarchical taxonomy to help the consolidation of SA area. The taxonomy aims at covering the addressed problems and methods in the SA field.

This taxonomy is a filtered union of various classifications found in the literature with a proposal of nomenclatures standardization. As instance, a case study is presented with 20 SA-related articles classified according to the proposed taxonomy.

This taxonomy is very expressive because it covers many concepts and is also effective once it allows the distinction and categorization of the previous SA works.

To the best of the authors’ knowledge, the literature does not present such an expressive and effective classification as proposed in this paper. This new taxonomy allows for the navigation between the existing concepts in the SA field as well as, facilitates the search, comparison and indexing of papers already published.

The purpose of this paper is to investigate the effect of including letter repetition commonly found within social media text and its impact in determining the sentiment scores for two major airlines in Malaysia.

A Sentiment Intensity Calculator (SentI-Cal) was developed by assigning individual weights to each letter repetition, and tested it using data collected from official Facebook pages of the airlines.

Evaluation metrics indicate that SentI-Cal outperforms the baseline tool Semantic Orientation Calculator (SO-CAL), with an accuracy of 90.7 percent compared to 58.33 percent for SO-CAL.

A more accurate sentiment score allows airline services to easily obtain a better understanding of the sentiments of their customers, hence providing opportunities in improving their airline services.

Proposed mechanism calculates sentiment intensity of social media text by assigning individual weightage to each repeated letter and exclamation mark thus producing a more accurate sentiment score.