Deep Learning Based Cross Domain Sentiment Classification for Urdu Language

Abstract

Sentiment analysis is a widely researched area due to its various applications in customer services, brand monitoring, and market research. Automatic sentiment classification is an important but challenging task. Contrary to the English language, sentiment analysis for low-resource languages like Urdu is an under-explored research area. Most of the work on sentiment analysis in the Urdu language is domain-dependent where models are mostly trained and tested on the same dataset on limited domains. However, sentiments in different domains are expressed differently, and manually annotating the datasets for all possible domains is unfeasible. Training a sentiment classifier using annotated data on one domain and testing it on another domain results in poor performance as the terms appearing in the source domain (training data) might not appear in the target (testing data) domain. In this paper, we present a baseline method for cross-domain sentiment analysis in the Urdu language using two different domains. Feature extraction is performed using n-grams and word embedding techniques. Sentiment classification is performed using machine learning and deep learning classifiers. The proposed method achieves an accuracy, precision, recall, and F1 scores of 0.77, 0.83, 0.68, and 0.75, respectively.INDEX TERMS Cross-domain sentiment analysis; deep learning; Urdu language processing; feature engineering

Sentiment Analysis in Low-Resource Settings: A Comprehensive Review of Approaches, Languages, and Data Sources

“…One-hot encoding [35], [74] CBOW [96], , [97], [64], [68], [89], [75], [104] Skip Gram [80], [62], [68], [89] Embedding [89], [83], [69], [70], [91], [84], [127], [81],…”

“…A. Altaf et al, [35] highlighted that the majority of sentiment analysis research in the Urdu language relies on certain domains, and models are frequently tested and trained on the same dataset. for a small number of domains, to leverage the problem they develop a method model that adapts a cross-domain sentiment analysis in Urdu languages.…”

“…Nonetheless, challenges persist, particularly in accurately representing all languages within tokenizer frameworks due to variations in language characters and other linguistic factors. Data cleaning [95], [35], [96], [49], [62], [65], [58], [68], [67], [89], [74], [75], [76], [77], [88], [94], [100], [103], [104], [107], [80], [106], [81], [108], [109], [82], [53], [110], [111], [83], [92], [113], [114], [115], [116], [117] Stemming [51], [67], [104], [108],…”

“…Additionally, one-hot encoding is mentioned in a few like [35] and [74], suggesting its presence in certain studies despite it various limitations. Embedding techniques, including both traditional embeddings and contextual embeddings, emerge as popular choices, with numerous studies highlighting their effectiveness.…”

“…Sentiment classification methods are the methods used in categorizing text into polarities. They are classified into three categories: lexicon-based, machine learning (ML), and hybrid [12,24,[28][29][30][31][32][33][34][35]. However, the lexicon-based method relies on a predefined set of patterns, often referred to as a sentiment dictionary or lexicon, where each data entry is linked with a specific sentiment orientation [17,36].…”