Identification and handling of intensifiers for enhancing accuracy of Urdu sentiment analysis

Mukhtar, Neelam; Khan, Masood Alam; Chiragh, Nadia; Nazir, Shah

doi:10.1111/exsy.12317

Cited by 23 publications

(6 citation statements)

References 19 publications

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“…The sentiment analyzer achieves an accuracy of 83.4%, and this improves by 5.09% with the use of intensifiers. Another study by Mukhtar et al [23] investigated the impact of negations in the USA, using the same dataset as their earlier work on identification [22]. However, this time, with an overall accuracy of 78.32%, the improvement is seen around 4.4% when the negations are handled during the analysis.…”

Section: A Sentiment Classificationmentioning

confidence: 96%

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Cognitive Relationship-Based Approach for Urdu Sarcasm and Sentiment Classification

Khan,

Ahmed,

Siddiqui

et al. 2023

IEEE Access

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Humans have a natural tendency to express their emotions, but they are also skilled at using sarcasm to shape their feelings. In cognitive computing and natural language processing research, sentiment analysis and sarcasm detection are typically treated as separate tasks, with each text analyzed in isolation. However, this approach overlooks the connection between sentiment and sarcasm. We believe that sentiment and sarcasm are closely related and should be analyzed together to achieve a better understanding of context and natural language. In this paper, we propose a new framework that leverages the Cognitive Relationship (CR) between sarcasm and sentiment to improve the accuracy of classification. By taking into account the relationship between these two factors, we can achieve better results in sentiment analysis and sarcasm detection. We have also created a new and nearly balanced dataset for sentiment and sarcasm classification in standard Urdu that contains 7,000 tweets, which make up over 210K tokens. To gain a better understanding of the data, we conducted exploratory data analysis on words, hashtags, and emojis. The proposed methodology conducted a variety of classical machine learning classifiers and tested them with different variations of the dataset. After a thorough analysis of the results and errors, we found that the CR-based approach for sarcasm and sentiment classification performed better than the traditional stand-alone (SA) approach. Among the classifiers, Linear Regression and eXtreme Gradient Boosting proved to be the most effective. The sentiment classification based on CR has demonstrated a 9.3% enhancement compared to the stand-alone (SA) method while maintaining an overall improvement of approximately 22% compared to the baseline distribution. In the same way, the sarcasm classification based on CR has shown a 9.1% improvement over the SA approach and approximately 23.6% improvement over the baseline distribution.

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Section: A Sentiment Classificationmentioning

confidence: 96%

“…Mukhtar et al [22] showed SA in Urdu blogs by employing a rule-based technique with an emphasis maintained on the usage of intensifiers. In this regard, a sentiment corpus of 151 Urdu blogs is created which is consisting of 6,025 sentences.…”

Section: A Sentiment Classificationmentioning

confidence: 99%

Cognitive Relationship-Based Approach for Urdu Sarcasm and Sentiment Classification

Khan,

Ahmed,

Siddiqui

et al. 2023

IEEE Access

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“…Intensifiers enhance the emotional context of the word by increasing the degree of the adjective or adverbs [16]. These adjectives or adverbs may be positive or negative sentiment words.…”

Section: Intensifiersmentioning

confidence: 99%

Sentiment Analysis on Twitter Data: Comparative Study on Different Approaches

Rahman¹,

Sadat²,

Siddik³

2021

IJISA

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Social media has become incredibly popular these days for communicating with friends and for sharing opinions. According to current statistics, almost 2.22 billion people use social media in 2016, which is roughly one third of the world population and three times of the entire population in Europe. In social media people share their likes, dislikes, opinions, interests, etc. so it is possible to know about a person’s thoughts about a specific topic from the shared data in social media. Since, twitter is one of the most popular social media in the world; it is a very good source for opinion mining and sentiment analysis about different topics. In this research, SVM with different kernel functions and Adaboost are experimented using CPD and Chi-square feature extraction techniques to explore the best sentiment classification model. The reported average accuracy of Adaboost for Chi-square and CPD are 70.2% and 66.9%. The SVM radial basis kernel and polynomial kernel with Chi-square n-grams reported average accuracy of 73.73% and 68.67% respectively. Among the performed experimentation, SVM sigmoid kernel with Chi-square n-grams provided the maximum accuracy that is 74.4%.

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“…Although, several Urdu blogs are available online but there is no benchmark dataset available in Urdu language domain. Therefore, we have used the dataset developed by Mukhtar et al [11] for Urdu sentence classification. The dataset is tagged with positive, negative and neutral sentences.…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…It is the most frequently used language in Pakistan and is often used in other countries as well where people of Pakistan are living. Existing work for the text classification in Urdu language [11] is inadequate as it performs simple text classification to extract opinions without considering targets. Finding opinions is the only work that has been explored in Urdu language sentiment analysis without focusing on the targets of the opinions.…”

Section: Introductionmentioning

confidence: 99%

Extraction of Opinion Target Using Syntactic Rules in Urdu Text

Rana¹,

Bakht²,

Afzal³

et al. 2021

Intelligent Automation &Amp; Soft Computing

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Opinion target or aspect extraction is the key task of aspect-based sentiment analysis. This task focuses on the extraction of targeted words or phrases against which a user has expressed his/her opinion. Although, opinion target extraction has been studied extensively in the English language domain, with notable work in other languages such as Chinese, Arabic etc., other regional languages have been neglected. One of the reasons is the lack of resources and available texts for these languages. Urdu is one, with millions of native and non-native speakers across the globe. In this paper, the Urdu language domain is focused on to identify opinion targets from written Urdu texts. To accomplish this task, several syntactic rules are crafted to identify users' opinions and associated target words. These rules are crafted using the grammatical and linguistic context of the words in the sentence. To the best of our knowledge, there is no existing work available in the Urdu domain for opinion target extraction. The proposed methodology is evaluated on an Urdu language dataset and compared with an existing approach for the English language by applying the same technique. The experiments have demonstrated that the proposed approach achieves promising performance as compared to the applied English language domain approach.

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Identification and handling of intensifiers for enhancing accuracy of Urdu sentiment analysis

Cited by 23 publications

References 19 publications

Cognitive Relationship-Based Approach for Urdu Sarcasm and Sentiment Classification

Cognitive Relationship-Based Approach for Urdu Sarcasm and Sentiment Classification

Sentiment Analysis on Twitter Data: Comparative Study on Different Approaches

Extraction of Opinion Target Using Syntactic Rules in Urdu Text

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