A Study of Sentiment Analysis Task and It's Challenges

Pandey, Shubham V.; Deorankar, A. V.

doi:10.1109/icecct.2019.8869160

Cited by 16 publications

(6 citation statements)

References 5 publications

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“…In this study, we propose a hybrid framework for detecting the polarity of sentiments in Urdu using multiple deep neural network approaches and dependency-based Urdu language grammatical rules. This work is a continuation of our previous work (Pandey and Deorankar, 2019), in which we used multimodal SA. Three distinct datasets were used in our experiments: movie reviews, product reviews, and political reviews.…”

Section: Discussionmentioning

confidence: 92%

A Hybrid Dependency-Based Approach for Urdu Sentiment Analysis

Sehar

Kanwal

Dashtipur

et al. 2022

Preprint

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In this era of internet, it is evident that social media is one of the biggest platforms acting as a source of producing a huge amount of raw data on a daily basis that contains the opinion of people from different races, cultures, and age groups on a wide range of topics. Data that is produced on the social media platforms could be utilised by businesses to extract information for improving their services and to reach a wider set of audiences based on users' opinions being shared on these social media sites. In order to utilise this huge amount of unstructured data generated continuously on social media, a deep understanding of Natural Language Processing (NLP) is vital. Most of the approaches proposed so far for sentiment analysis (SA) are based on word co-occurrence frequencies that are inefficient in practical cases. Considering this research gap, this paper proposes a framework for concept-level SA for better SA. We developed our Urdu language dataset by collecting from YouTube, which comprises talks and reviews on different topics like movies, political, and commercial products. Further dataset was integrated with language rules and Deep Neural Networks (DNN) to optimise the polarity detection. For SA, when a predefined rule is triggered, the framework enables the sentiments to flow from words to concepts based on dependency relations among different words of a sentence on the basis of Urdu language grammatical rules. When any of the predefined patterns is not triggered, the framework switches to its sub-symbolic counterpart and passes data to DNN for performing the classification of input sentence. The proposed framework outperforms state-of-the-art approaches, including Long short-term memory (LSTM), Convolutional Neural Network (CNN), State Vector Machine (SVM), Linear Regression (LR), and Multilayer Perceptrons (MLP), with a margin of 6–7%, respectively, when employed on our Urdu dataset.

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Section: Discussionmentioning

confidence: 92%

A Hybrid Dependency-Based Approach for Urdu Sentiment Analysis

Sehar

Kanwal

Dashtipur

et al. 2022

Preprint

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“…Sentiment analysis is the study of public opinion, feelings, sentiments, and attitudes about various objects using written language [14]. The major assignment in sentiment analysis is to classify whether a given opinion extracted from the public space such as a miniblog is positive or negative, as well as the depth of the sentiment.…”

Section: Sentiment Analysismentioning

confidence: 99%

Textual Analysis for Public Sentiment Toward National Police Using CRISP-DM Framework

Sudar,

Imbenay,

Budi

et al. 2024

RIA

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Nowadays, public opinion toward the National Police's (POLRI) image is deteriorating. With the explosive growth of social media in Indonesia, opinions on POLRI-related present-day issues on Twitter easily go viral, influencing sentiments among individuals regarding Indonesian law enforcement. Negative sentiments, at some point, may lead to the undervaluation of law enforcement and the failure of the legal system. Therefore, sentiment analysis on Twitter is essential for gaining considerable insights into public views and attitudes on POLRI-related topics. This research is to determine the most effective approaches between Lexicon, a natural language processing method that relies on a corpus, and machine learning, which contains Naive-Bayes, Support Vector Machine (SVM), Random Forest, and Logistic Regression (LR). These approaches have differences in classification types: probability and linearity. To organize the research process, the Cross-Industry Standard Process for Data Mining (CRISP-DM) Framework, which comprises five data mining activities, was employed. The confusion matrix was used as the model performance measurement, with Naive-Bayes emerging as the best among all the tested models. Additionally, the subjects related to POLRI were developed using topic modeling, generating three topics: street police or police station, police acknowledgment in neighborhood activities, and the activity of contacting the police.

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“…Arabic NLP works; including sentiment analysis, is still in their infancy compared to other languages. Despite that Arabic language is ranked as the fourth most used language having more than 188 million Arab internet users [2] . This is due to several challenging factors; some are related to the sentiment analysis problems and others are related to the Arabic language itself.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, it became crucial to capture and analyze users' opinions and sentiments toward different topics whether it is positive or negative. The process of analyzing opinionated data to deliver new insights is called sentiment analysis [2]. Techopedia defined it as "a type of data mining that measures the inclination of people's opinions through natural language processing (NLP), computational linguistics and text analysis, which are used to extract and analyze subjective information from the Web -mostly social media and similar sources" [1] .…”

Section: Introductionmentioning

confidence: 99%

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Annotated Data Augmentation for Arabic Sentiment Analysis using Semi-Supervised GANs

AlNashash

Al-Taani

Abu-Soud

2022

Preprint

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With the increasing number of web users, a tremendous amount of data is being generated in an enormous and dynamic way. In this regard, analyzing the sentiment of the user’s generated data is becoming essential to reveal valuable insights. However, analyzing this massive amount of data requires a lot of manual effort. Therefore, sentiment analysis systems automate the analysis of the generated text which helps in extracting people’s opinions and attitudes. The great success of sentiment analysis systems is due to the availability of a copiously annotated corpus. Unfortunately, Arabic Language does not possess such an abundance of annotated data. Obtaining annotated sentiment corpus is an expensive and challenging task due to the huge amount of corpus required to train efficient sentiment analyzers. The purpose of this research is to tackle the problems of Sentiment Analysis for Arabic language and the inadequate annotated data available for this task. Owing to the limited resources for Arabic sentiment analysis, this work is motivated to investigate data augmentation using GAN. GANs showed breakthrough results in data augmentation due to their capabilities of generating realistic data samples. In this work, we propose using Semi-Supervised GANs for Arabic sentiment analysis to minimize the number of annotated data required for this task. As a result, we were able to prove that GAN can be used to augment realistic textual data samples that can improve the performance of the sentiment analysis task. Results also showed that Semi-Supervised GAN can drastically reduce the requirement of manually annotated samples. In this context, our model successfully excels the task of Arabic Sentiment Analysis with only 10–30% of dataset being manually labeled.

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A Study of Sentiment Analysis Task and It's Challenges

Cited by 16 publications

References 5 publications

A Hybrid Dependency-Based Approach for Urdu Sentiment Analysis

A Hybrid Dependency-Based Approach for Urdu Sentiment Analysis

Textual Analysis for Public Sentiment Toward National Police Using CRISP-DM Framework

Annotated Data Augmentation for Arabic Sentiment Analysis using Semi-Supervised GANs

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