Sentiment Analysis on Urdu Tweets Using Markov Chains

Nasim, Zarmeen; Ghani, Sayeed

doi:10.1007/s42979-020-00279-9

Cited by 20 publications

(16 citation statements)

References 18 publications

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“…Their experiments revealed that CNN with multiple filters (3,4,5) achieved the highest rank, while BilSTM outperformed LSTM and CLSTM. In [27], the authors used a single layer CNN with multiple filters for document-level text classification and found it superior to the baseline methods. Asim et al [29] assessed performance of state-of-the-art ML, DL, and hybrid model for document classification.…”

Section: Deep Learning Based Approachmentioning

confidence: 99%

UTSA: Urdu Text Sentiment Analysis Using Deep Learning Methods

2021

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The Internet has seen substantial growth of regional language data in recent years. It enables people to express their opinion by incapacitating the language barriers. Urdu is a language used by 170.2 million people for communication. Sentiment analysis is used to get insight of people opinion. In recent years, researchers' interest in Urdu sentiment analysis has grown. Application of deep learning methods for Urdu sentiment analysis has been least explored. There is a lot of ground to cover in terms of text processing in Urdu since it is a morphologically rich language. In this paper, we propose a framework for Urdu Text Sentiment Analysis (UTSA) by exploring deep learning techniques in combination with various word vector representations. The performance of deep learning methods such as Long Short-Term Memory (LSTM), attention-based Bidirectional LSTM (BiLSTM-ATT), Convolutional Neural Networks (CNN) and CNN-LSTM is evaluated for sentiment analysis. Stacked layers are applied in sequential model LSTM, BiLSTM-ATT, and C-LSTM. In CNN, various filters are used with single convolution layer. Role of pre-trained and unsupervised self-trained embedding models is investigated on sentiment classification task. The results obtained show that the BiLSTM-ATT outperformed other deep learning models by accomplishing 77.9% accuracy and 72.7% F1 score.

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Section: Deep Learning Based Approachmentioning

confidence: 99%

UTSA: Urdu Text Sentiment Analysis Using Deep Learning Methods

2021

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“…In this section is a brief overview of the literature. For predicting the emotion of Urdu tweets, Zarmeen Nasim and Sayeed Ghani [1] suggested an emotion analysis approach according to the Markov chains. The recovery of valuable information from a vast volume of data is one of the fields of text mining.…”

Section: Related Workmentioning

confidence: 99%

Text Clustering using K-MEAN

2021

IJATCSE

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Document clustering allows the user to add similar documents to a group. For many years, it has been a fascinating research topic, developed various methods and techniques. However, the study focuses mostly on English and high-resource languages. About Pakistan national anthems, this research gives an experimental estimation of clustering techniques. Because of its short length, thematically clustering Anthem is a difficult task. This paper extracted various characteristics, including stop-words, stemming, corpus tokenization, noise removal, and TF-IDF features from the anthem, and the clustering was conducted using the K-Means algorithm. The results show that a clustering strategy paired with a K-mean clustering algorithm with TF-IDF features has already been used. The dataset is available on GitHub (https://www.kaggle.com/lucasturtle/national-anthems-of- the-world )

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“…Sentiment analysis has been an essential mechanism in practical life when provides peoples with others' opinions; in which this could be effective in others' decision making for example in selecting a movie to watch, or purchasing a product. 1,2 Many e-commerce and commercial websites (e.g., Amazon for online purchase of their goods), or YouTube (e.g., for online video sharing) or other social media platforms apply automated sentiment analysis methods to catch opinions of potential consumers or purchasers on their products and increase the consumer purchase decision. Hence, applying automated sentiment analysis is increasing between local stores and enterprises to maintain positive reviews of their services or products while their user data are exponentially generated.…”

Section: Introductionmentioning

confidence: 99%

Sentiment analysis incorporating convolutional neural network into hiddenMarkovmodel

Najafabadi

2024

Computational Intelligence

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The analysis of sentiments and mining of opinions have become more and more important in years because of the development of social media technologies. The methods that utilize natural language processing and lexicon‐based sentiment analysis techniques to analyze people's opinions in texts require the proper extraction of sentiment words to ensure accuracy. The current issue is tackled with a novel perspective in this paper by introducing a hybrid sentiment analysis technique. This technique brings together Convolutional Neural Network (CNN) and Hidden Markov Models (HMMs), to accurately categorize text data and pinpoint feelings. The proposed method involves 1D convolutional‐layer CNN to extract hidden features from comments and applying HMMs on a feature‐sentence matrix, allowing for the utilization of word sequences in extracting opinions. The method effectively captures diverse text patterns by extracting a range of features from texts using CNN. Text patterns are learned using text HMM by calculating the probabilities between sequences of feature vectors and clustering feature vectors. The paper's experimental evaluation employs benchmark datasets such as CR, MR, Subj, and SST2, demonstrating that the proposed method surpasses existing sentiment analysis techniques and traditional HMMs. One of its strengths is to analyze a range of text patterns and identify crucial features that recognize the emotion of different pieces of a sentence. Additionally, the research findings highlight the improved performance of sentiment analysis tasks through the strategic use of zero padding in conjunction with the masking technique.

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Sentiment Analysis on Urdu Tweets Using Markov Chains

Cited by 20 publications

References 18 publications

UTSA: Urdu Text Sentiment Analysis Using Deep Learning Methods

UTSA: Urdu Text Sentiment Analysis Using Deep Learning Methods

Text Clustering using K-MEAN

Sentiment analysis incorporating convolutional neural network into hiddenMarkovmodel

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