Morphological evaluation and sentiment analysis of Punjabi text using deep learning classification

Singh, Jaspreet; Singh, Gurvinder; Singh, Rajinder; Singh, Prithvipal

doi:10.1016/j.jksuci.2018.04.003

Cited by 30 publications

(19 citation statements)

References 8 publications

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“…Singh et al have combined sentiment analysis and morphological assessment in Punjabi language, using DL. The accuracy rate of the model, created using DL and morphological text classification with 275 suicide cases in Punjab, was 95.45% [20].…”

Section: Related Workmentioning

confidence: 99%

Sentiment Analysis on Social Media Reviews Datasets with Deep Learning Approach

Başarslan

Kayaalp

2021

Sakarya University Journal of Computer and Information Sciences

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Thanks to social media, people are now able to leave guiding comments quickly about their favorite restaurants, movies, etc. This has paved the way for the field of sentiment analysis, which brings together various disciplines. In this study, Yelp restaurant reviews and IMDB movie reviews dataset were used together with the data collected from Twitter. Word2Vec (W2V), Global Vector (GloVe) and Bidirectional Encoder Representation (BERT) word embedding methods, Term Frequency-Reverse Document Frequency (TF-IDF), and the Bag-of-Words (BOW) were used on these datasets. Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), Support Vector Machine (SVM), and Naive Bayes (NB) were used in the sentiment analysis models. Accuracy, F-measure (F), Sensitivity (Sens), Precision (Pre), and Receiver Operating Characteristics (ROC) were used in the evaluation of the model performance. The Accuracy rates of the models created by the Machine Learning (ML) and Deep Learning (DL) methods using the IMDB dataset were in the range of 81%-90% and 84%-94%, respectively. These rates were in the range of 80%-86% and 81%-89% for the Yelp dataset, and in the range of 75%-79% and 85%-98% for the Twitter dataset. The models that incorporated the BERT word embedding method have the best performance, compared to the other models with ML and DL. Therefore, BERT method is recommended for this type of analysis in future studies.

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Section: Related Workmentioning

confidence: 99%

Sentiment Analysis on Social Media Reviews Datasets with Deep Learning Approach

Başarslan

Kayaalp

2021

Sakarya University Journal of Computer and Information Sciences

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“…(Sharma, 2017) has mentioned separation of dependent and independent clauses from a sentence in the Punjabi language using some rules and Part of Speech (POS) (Gupta et al, 2011) tagging. (Singh et al, 2018) have elaborated suicide case of farmers in Punjab which is analyzed using morphological and sentiment analysis in the Gurmukhi script; later on, deep neural network is trained using feature set and splitter to split training and test sets. (Sharma, 2019) has developed syntactic analysis system in the Punjabi for language-based compound sentences.…”

Section: Related Workmentioning

confidence: 99%

Particle Swarm Optimization for Punjabi Text Summarization

Jain

Yadav

Arora

2021

International Journal of Operations Research and Information Systems

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Particle swarm optimization (PSO) algorithm is proposed to deal with text summarization for the Punjabi language. PSO is based on intelligence that predicts among a given set of solutions which is the best solution. The search is carried out by extremely high-speed particles. It updates particle position and velocity at the end of iteration so that during the development of generations, the personal best solution and global best solution are updated. Calculation within PSO is performed using fitness function which looks into various statistical and linguistic features of the Punjabi datasets. Two Punjabi datasets—monolingual Punjabi corpus from Indian Languages Corpora Initiative Phase-II and Punjabi-Hindi parallel corpus—are considered. The parallel corpus comprises 1,000 Punjabi sentences from the tourism domain while monolingual corpus contains 30,000 Punjabi sentences of the general domain. ROUGE measures evaluate summary where the highest measure, ROUGE-1, is achieved for parallel corpus with precision, recall, and F-measure as 0.7836, 0.7957, and 0.7896, respectively.

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“…The importance of a word depends on its frequency inside its sentence [28] or the number of times a particular word occurs in the sentence [29]. However, the exact matching of terms was used, and the partial matching where words are similar but not identical was not considered.…”

Section: Sentence Retrieval Using Sequence Similaritymentioning

confidence: 99%

Improving Sentence Retrieval Using Sequence Similarity

2020

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Sentence retrieval is an information retrieval technique that aims to find sentences corresponding to an information need. It is used for tasks like question answering (QA) or novelty detection. Since it is similar to document retrieval but with a smaller unit of retrieval, methods for document retrieval are also used for sentence retrieval like term frequency—inverse document frequency (TF-IDF), BM 25 , and language modeling-based methods. The effect of partial matching of words to sentence retrieval is an issue that has not been analyzed. We think that there is a substantial potential for the improvement of sentence retrieval methods if we consider this approach. We adapted TF-ISF, BM 25 , and language modeling-based methods to test the partial matching of terms through combining sentence retrieval with sequence similarity, which allows matching of words that are similar but not identical. All tests were conducted using data from the novelty tracks of the Text Retrieval Conference (TREC). The scope of this paper was to find out if such approach is generally beneficial to sentence retrieval. However, we did not examine in depth how partial matching helps or hinders the finding of relevant sentences.

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Morphological evaluation and sentiment analysis of Punjabi text using deep learning classification

Cited by 30 publications

References 8 publications

Sentiment Analysis on Social Media Reviews Datasets with Deep Learning Approach

Sentiment Analysis on Social Media Reviews Datasets with Deep Learning Approach

Particle Swarm Optimization for Punjabi Text Summarization

Improving Sentence Retrieval Using Sequence Similarity

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