Sentimental analysis of Indian regional languages on social media

Rakshitha, Kakuthota; Ramalingam, Hemalatha; Pavithra, M; Advi, H D; Hegde, Maithri

doi:10.1016/j.gltp.2021.08.039

Cited by 24 publications

(13 citation statements)

References 8 publications

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“…A lexicon-based approach to know the polarity of data is much in trend. In paper (Rakshitha et al., 2021 ), TextBlob is used to analyse the polarity of customer reviews in five Indian regional languages. Researchers in Yousefinaghani et al.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The analysis of Twitter data has become a powerful tool to capture dynamic information related to public perception, which can serve as useful information for decision makers (De Rosis et al., 2021 ). Some existing studies (De Rosis et al., 2021 ; Rakshitha et al., 2021 ; Yousefinaghani et al., 2021 ) on Twitter sentiment analysis are based on a lexicon-based approach. Such a technique, when combined with a statistical/machine learning approach, can lead to a more effective polarity classification (Cambria et al., 2017 ).…”

Section: Introductionmentioning

confidence: 99%

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Deep learning based sentiment analysis of public perception of working from home through tweets

Vohra

Garg

2022

J Intell Inf Syst

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Nowadays, we are witnessing a paradigm shift from the conventional approach of working from office spaces to the emerging culture of working virtually from home. Even during the COVID-19 pandemic, many organisations were forced to allow employees to work from their homes, which led to worldwide discussions of this trend on Twitter. The analysis of this data has immense potential to change the way we work but extracting useful information from this valuable data is a challenge. Hence in this study, the microblogging website Twitter is used to gather more than 450,000 English language tweets from 22nd January 2022 to 12th March 2022, consisting of keywords related to working from home. A state-of-the-art pre-processing technique is used to convert all emojis into text, remove duplicate tweets, retweets, username tags, URLs, hashtags etc. and then the text is converted to lowercase. Thus, the number of tweets is reduced to 358,823. In this paper, we propose a fine-tuned Convolutional Neural Network (CNN) model to analyse Twitter data. The input to our deep learning model is an annotated set of tweets that are effectively labelled into three sentiment classes, viz. positive negative and neutral using VADER (Valence Aware Dictionary for sEntiment Reasoning). We also use a variation in the input vector to the embedding layer, by using FastText embeddings with our model to train supervised word representations for our text corpus of more than 450,000 tweets. The proposed model uses multiple convolution and max pooling layers, dropout operation, and dense layers with ReLU and sigmoid activations to achieve remarkable results on our dataset. Further, the performance of our model is compared with some standard classifiers like Support Vector Machine (SVM), Naive Bayes, Decision Tree, and Random Forest. From the results, it is observed that on the given dataset, the proposed CNN with FastText word embeddings outperforms other classifiers with an accuracy of 0.925969. As a result of this classification, 54.41% of the tweets are found to show affirmation, 24.50% show a negative disposition, and 21.09% have neutral sentiments towards working from home.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep learning based sentiment analysis of public perception of working from home through tweets

Vohra

Garg

2022

J Intell Inf Syst

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“…A general approach to address these challenges is lacking. Although there is promising progress in some Indian languages like Tamil, Urdu [107], [108] and Telugu [109] and Iranian languages like Persian [85], [110], much is still required to develop models that employ deep learning techniques [111]. Also, they attempted to address challenges in Persian language by applying DL methods which only achieved the f-score of 55.5%.…”

Section: Emerging Msa Areasmentioning

confidence: 99%

Multilingual Sentiment Analysis for Under-Resourced Languages: A Systematic Review of the Landscape

2023

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Sentiment analysis automatically evaluates people's opinions of products or services. It is an emerging research area with promising advancements in high-resource languages such as Indo-European languages (e.g. English). However, the same cannot be said for languages with limited resources. In this study, we evaluate multilingual sentiment analysis (MSA) techniques for under-resourced languages and the use of high-resourced languages to develop resources for MSA in low-resource languages, with the ultimate goal of identifying appropriate strategies for future MSA investigations. We report over 35 studies with different languages demonstrating an interest in developing MSA models for under-resourced languages in a multilingual context. Furthermore, we illustrate the drawbacks of each strategy used for the MSA task. Our focus is critically comparing MSA methods and employed datasets and identifying research gaps. Our comparative analysis study contributes to theoretical literature reviews with complete coverage of MSA studies from 2008 to date. Furthermore, we demonstrate how MSA studies have grown tremendously. Finally, because most studies propose MSA methods based on deep learning approaches, we offer a deep learning framework for MSA that does not rely on machine translation systems. According to the metaanalysis (PRISMA) protocol of this literature review, we found that, in general, just over 60% of the studies have used deep learning frameworks, which significantly improved the MSA performance. Therefore, deep learning methods are recommended for the development of MSA for under-resourced languages.

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“…The severity of a term affects whether it alters the following word or phrase. Adverbs are often employed in English as modifiers (e.g., "excess amount") [43,44].…”

Section: Sentiment Labellingmentioning

confidence: 99%

Cluster-Based Knowledge Graph and Entity-Relation Representation on Tourism Economical Sentiments

et al. 2022

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The tourism industry has experienced fast and sustainable growth over the years in the economic sector. The data available online on the ever-growing tourism sector must be given importance as it provides crucial economic insights, which can be helpful for consumers and governments. Natural language processing (NLP) techniques have traditionally been used to tackle the issues of structuring of unprocessed data, and the representation of the data in a knowledge-based system. NLP is able to capture the full richness of the text by extracting the entity and relationship from the processed data, which is gathered from various social media platforms, webpages, blogs, and other online sources, while successfully taking into consideration the semantics of the text. With the purpose of detecting connections between tourism and economy, the research aims to present a visual representation of the refined data using knowledge graphs. In this research, the data has been gathered from Twitter using keyword extraction techniques with an emphasis on tourism and economy. The research uses TextBlob to convert the tweets to numeric vector representations and further uses clustering techniques to group similar entities. A cluster-wise knowledge graph has been constructed, which comprises a large number of relationships among various factors, that visualize entities and their relationships connecting tourism and economy.

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Sentimental analysis of Indian regional languages on social media

Cited by 24 publications

References 8 publications

Deep learning based sentiment analysis of public perception of working from home through tweets

Deep learning based sentiment analysis of public perception of working from home through tweets

Multilingual Sentiment Analysis for Under-Resourced Languages: A Systematic Review of the Landscape

Cluster-Based Knowledge Graph and Entity-Relation Representation on Tourism Economical Sentiments

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