Grammar rule-based sentiment categorisation model for classification of Tamil tweets

Ravishankar, T. Nadana; Shriram, Revati

doi:10.1504/ijista.2018.10012884

Cited by 4 publications

(3 citation statements)

References 7 publications

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“…Similarly, researchers made some significant attempts to identify fake reviews and other similar tasks in the literature. These initiatives include the detection of fake news in Thailand [26], the identification of Tamil swear words, the detection of spurious reviews in e-commerce, and opinion mining [27] [28].…”

Section: Related Workmentioning

confidence: 99%

BERT Model for Roman Urdu Fake Review Identification

Ullah,

Saeed,

et al. 2023

Preprint

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The variety of e-commerce and online business environments makes spotting fraud or fake reviews challenging. The goal of this research is to create methods that could ensure the authenticity of reviews. Numerous endeavors have already been made by the research community to address this problem. Unfortunately, these efforts were confined to a specific number of languages, such as English, Arabic, and a few others. Roman Urdu is being used across the subcontinent. For this task, this language hasn't been properly investigated. On the other hand, Transformer-based models have demonstrated tremendous success over the past few years for a variety of Natural Language Processing applications. Transformer-based models, however, have not been investigated for the goal of examining fake Roman Urdu reviews. This study compares multiple transfer-based models, including BERT_base and BERT_large with five deep learning models Simple RNN, LSTM, Bidirectional LSTM, GRU, and Bidirectional GRU aims to close this research gap. The evaluation was conducted using frequently applied metrics, i.e., ROC-Accuracy, F_1, Precision, and Recall. The BERT_large with Bi-LSTM model produced the best results (ROC-ACC=0.94 and F_1=0.94). Results from a previous study using the same corpus were improved by 6% in this investigation.

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Section: Related Workmentioning

confidence: 99%

BERT Model for Roman Urdu Fake Review Identification

Ullah,

Saeed,

et al. 2023

Preprint

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“…Most approaches to the syntactic analysis of tweets are dedicated to sentiment analysis. [19] presents an approach for genre categorization of movies according to user sentiment. They used different techniques to find the sentiments of the users.…”

Section: B Syntactic Analysis Of Tweetsmentioning

confidence: 99%

Little Known POI Category Estimation via Syntactical Knowledge Graph Generated via Tweets

Rajaonarivo,

Mine,

Arakawa

2023

2023 18th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)

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Local events are important for the vitality and dynamism of a city. However, some events or sites are lesserknown or only known by the local community. The information about them is then limited or not available. To address this issue, we propose an approach to automatically generate knowledge graphs from information posted on social networks and data on human mobility. This graph is generated from the tweets using the syntactic relationships between the verbs, nouns, and adjectives. Its exploitation would allow to estimate the categories of lesser-known points of Interest (POIs) or events. It can also allow to generate sentences and to answer questions that tourists may ask. The specificity of our approach lies in the Graph Convolutional Network (GCN)-based encoding of POIs with a graph of verbs, nouns, and adjectives and the estimation of POI categories using the proposed encoding. Experimental results show that our approach performs better than approaches in the literature.

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“…As a result of this, SA is an effective instrument that may be utilized in the decision-making process in corporations [2]. That is why text data must be classified because it is one of the important processes in managing the produced documents [3]. In particular, document management and retrieval is complex without effective classification.…”

Section: Introductionmentioning

confidence: 99%

Machine learning-based approach for sentiment analysis from recorded social media movie reviews

Jassim,

Abd,

Omri

2023

Preprint

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The goal of Sentiment Analysis (SA), especially in social media, is to identify useful information in large amounts of unstructured data from many different sources. Text mining involves extracting revision texts and categorizing them by positive and negative attitudes. However, the value of the extracted features also lies in those that contribute most to the classification process. That's where dimensional reduction comes into play to remove noise and reduce the high area of the feature while maintaining the required accuracy. As part of this work, we propose a new approach for extracting words from a specific text and then classifying them. Thanks to a phase of pre-processing and extraction of words depending on the frequency. For this purpose, we considered a test group database of 50,000 film reviews, of which 25,000 were rated positive and 25,000 negatives. We have selected 4,000 words that have an impact on the feelings of the documents, To predict the rating which is based on a textual customer review. We evaluated the algorithms we proposed, as part of this approach, and compared the effectiveness of the following machine learning techniques as classifiers for SA: extreme gradient boosting (XGBoost), artificial neurons (ANN), and stochastic gradient descent (SGD). with a vectorial construction by applying the techniques of ”Term Frequency” (TF) and that of the ”Term frequency-Inverse Document Frequency” (TF-IDF). We noticed that our approach based on the SGD algorithm recorded the best performance. Indeed, we recorded an accuracy of 88.093\%, a precision of 89\%, a recall of 86\%, and an F1-score of 88\%. \textbf{Keywords :} Sentiment analysis; Opinion mining; movie review; classification; Machine learning; XGBoost; Artificial neural network; stochastic gradient descent.

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Grammar rule-based sentiment categorisation model for classification of Tamil tweets

Cited by 4 publications

References 7 publications

BERT Model for Roman Urdu Fake Review Identification

BERT Model for Roman Urdu Fake Review Identification

Little Known POI Category Estimation via Syntactical Knowledge Graph Generated via Tweets

Machine learning-based approach for sentiment analysis from recorded social media movie reviews

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