Lexicon-based approach outperforms Supervised Machine Learning approach for Urdu Sentiment Analysis in multiple domains

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

doi:10.1016/j.tele.2018.08.003

Cited by 84 publications

(44 citation statements)

References 15 publications

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“…Trinh et al [6] constructed an emotions dictionary to classify sentiments expressed in Facebook comments written in the Vietnamese language. Mukhtar et al [7] presented a comparison of the lexicon-based approach of sentiment analysis against the supervised machine learning method. The experiments were conducted on Urdu blogs dataset.…”

Section: Lexicon-based Methodsmentioning

confidence: 99%

“…Mongolian affixes were identified using hidden Markov models to perform segmentation. Lexicon-based Urdu [3] Lexicon-based Urdu [4] Lexicon-based Arabic [5] Lexicon-based Arabic [6] Lexicon-based Vietnamese [7] Lexicon-based Urdu [8] Supervised learning-based Roman-Urdu [1] Supervised learning-based Urdu [9] A supervised approach using hidden Markov chains English [10] A supervised approach using hidden Markov chains English [11] An unsupervised approach using clustering English [12] An unsupervised approach using clustering English Document clustering is the task of clustering similar documents together. Goyal et al [15] used a Markov based model to cluster documents containing short-length text.…”

Section: Applications Of Markov Chain In Nlpmentioning

confidence: 99%

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Sentiment Analysis on Urdu Tweets Using Markov Chains

Nasim

Ghani

2020

SN COMPUT. SCI.

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This paper presents a sentiment analysis approach based on Markov chains for predicting the sentiment of Urdu tweets. Sentiment analysis has been a focus of natural language processing (NLP) research community from the past few decades. The reason for this growing interest is twofold. First, the complexity involved in identifying sentiment from the unstructured text makes it a challenging problem for the research community. Second, sentiment analysis has a wide variety of applications ranging from industry to academia has made it a popular area in the research field of NLP. However, very little work has been done on sentiment analysis for the low resource languages which include Urdu, Bengali, Hindi, and other Asian languages. This work focuses on developing a 3-class (positive, negative, and neutral) sentiment classification model for the Urdu language. The experiments were conducted on the labeled corpus of Urdu tweets extracted from the Twitter network. One of the main contributions of this research includes the development of a large labeled corpus of Urdu Tweets for sentiment analysis. To the best of our knowledge, there is no such corpus available publicly in the Urdu Language. The labeled dataset is available on GitHub (https ://githu b.com/zarme en92/urdut weets). Furthermore, the results showed that the proposed approach outperforms the lexicon-based and traditional machine learning-based approaches of sentiment analysis.

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Section: Lexicon-based Methodsmentioning

confidence: 99%

Section: Applications Of Markov Chain In Nlpmentioning

confidence: 99%

Sentiment Analysis on Urdu Tweets Using Markov Chains

Nasim

Ghani

2020

SN COMPUT. SCI.

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“…The third approach is a hybrid approach that combines the first two approaches together for sentiment classification. Generally, the machine learning-based approach is more accurate, but it needs much more time to label the data [46]. In contrast, the dictionary-based approach has advantage of requiring no training data to classify the sentiment, and its computing time is much faster than that of machine learning [47].…”

Section: B Approaches To Sentiment Analysismentioning

confidence: 99%

Sentiment Analysis of Danmaku Videos Based on Naïve Bayes and Sentiment Dictionary

Jin

2020

IEEE Access

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Danmaku video provides a platform for users to communicate online while watching videos. Danmaku is a live commenting function where the comments related to the video being screened are created by users and prominently shown in real-time on the video screen. These live comments contain complex and rich sentiments, reflecting users' instant opinions and feelings on video programs. In some sense, danmaku provides emotional timing information about video data, and it also offers an innovative mean to analyze video data. However, existing sentiment classification methods are not suitable for danmaku data analysis. To solve this problem, this paper constructs a danmaku sentiment dictionary and presents a new method using sentiment dictionary and Naïve Bayes for the sentiment analysis of danmaku reviews. The method is greatly helpful in supervising the overall emotional orientation of a danmaku video and predicting its popularity. Through the processes of extracting emotional information from a danmaku video, classifying sentiment and visualizing data, the time distribution of the seven sentiment dimensions can be obtained. In addition, a weight calculation can be conducted for classifying the sentiment polarity of danmaku reviews. Experimental results show that the proposed method has a significant effect on sentiment score and polarity detection.

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“…In addition, there are some studies of the construction of language-specific sentimental lexicons, such as Urdu [28], Malay [29] and Slovene [30]. Note that the above mentioned sentimental lexicon resources cannot always recognize sentimental words in specific areas and cannot distinguish the diversity of lexical sentiments in different contexts.…”

Section: Related Work a Resources Of Existing Sentiment Lexiconsmentioning

confidence: 99%

The Construction of Sentiment Lexicon Based on Context-Dependent Part-of-Speech Chunks for Semantic Disambiguation

2020

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Sentiment lexicon, which provides sentiment information for words, plays an important role in sentiment analysis task. Currently, most of sentiment lexicons have only one sentiment polarity for each word and ignore sentimental ambiguity. In this paper, we propose to construct the sentiment lexicon based on context-dependent part-of-speech (POS) chunks, namely CP-chunks, which aims at solving the ambiguity of lexical sentiments. Given that the POS of context has impact on the word polarity and intensity, we take CP-chunks as an unit to do sentiment calculation. Our method is evaluated through the classification task of text sentiment. The experiment results indicate that, in comparison with the existing methods, the applicability of our method is more stable and balanced for both the positive and negative polarities corpora, and the accuracy of our method reaches 82% for the sentiment classification of a domain-specific corpus.

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Lexicon-based approach outperforms Supervised Machine Learning approach for Urdu Sentiment Analysis in multiple domains

Cited by 84 publications

References 15 publications

Sentiment Analysis on Urdu Tweets Using Markov Chains

Sentiment Analysis on Urdu Tweets Using Markov Chains

Sentiment Analysis of Danmaku Videos Based on Naïve Bayes and Sentiment Dictionary

The Construction of Sentiment Lexicon Based on Context-Dependent Part-of-Speech Chunks for Semantic Disambiguation

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