Sentiment analysis is a widely researched area due to its various applications in customer servic... more 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. 14 15 157 lexicon-based method for SA in the Persian language using 158 a dataset of mobile reviews. The authors extract the aspects 159 from the reviews using the combination of 'noun adjective' 160 pair or 'nouns adverbs adjective' pair using a lexicon. They 161 also consider the impact of intensifiers on the reviews and 162 present a visual summary of aspects in the reviews. The proposed method outperforms the previous studies in terms 164 of accuracy. Aye and Aung [24] perform SA on the Myanmar 165 language using a lexicon-based approach using a dataset of 166 500 restaurant reviews collected from OSNs. The authors 167 develop a lexicon using a dictionary-based approach. They 168 identify sentiment targets and assign polarity to the respective 169 sentiments of the targets. The identification of aspect terms 170 is context-independent. The performance of the automatic 171 polarity extraction method is compared with manually anno-172 tated reviews. Results of the proposed technique show high 173 accuracy. 174 Alqaryouti et al. [25] perform aspect-based SA using lexi-175 con and rule-based approaches in the Arabic language. The 176 authors discuss the aspect categories based on the stan-177 dards provided by mobile companies. They extract implicit 178 and explicit aspects from the lexicons, match the opinion 179 words and aspects with the lexicon to find the category of 180 the aspects, and assign the polarity of the opinion words 181 using lexicons based on devised rules. The proposed method 182 achieves an accuracy of 93%. Ibrahim et al. [26] develop a 183 lexicon to perform the SA of idioms in the Arabic lan-184 guage. The authors collect data manually through APIs con-185 sisting of proverbs, idioms, phrases, etc., and annotate it 186 through different annotators. The developed lexicon consists 187 of four columns, i.e., proverbs/idioms, English translation of 188 proverbs, Buck Walter, and polarity. They detect the proverbs 189 using bi-grams to six-grams and similarity between two texts 190 is determined using cosine similarity and the Levenshtein 191 distance algorithm. In the extraction phase, heuristic rules are 192 used to classify sentiments to avoid redundancy. To detect 193 polarity, positive proverbs are replaced by positive phrases, 194 and negative proverbs are replaced by negative phrases. The 195 results show an accuracy of 81.60% when the n-gram model 196 is used with cosine similarity, an accuracy of 86.12% when 197 n-gram is used with the edit distance algorithm, and an 198 accuracy of 98.62% when n-gram, cosine similarity, and edit 199 distance are used collectively. 200 B. MACHINE LEARNING APPROACH 201 Mehmood et al. [18] propose a novel feature spamming 202 approach to assign weights to terms in Roman Urdu which 203 helps to extract the most relevant and useful information 204 from data. Firstly, they identify important features using term 205 utility criteria (used to assign higher scores to significant 206 topics and vice versa). Furthermore, these distinctive features 207 are spammed using spamming factor which is adjusted by 208 user-defined hyperparameters while the weights of all other 209
Sentiment analysis is a widely researched area due to its various applications in customer servic... more 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. 14 15 157 lexicon-based method for SA in the Persian language using 158 a dataset of mobile reviews. The authors extract the aspects 159 from the reviews using the combination of 'noun adjective' 160 pair or 'nouns adverbs adjective' pair using a lexicon. They 161 also consider the impact of intensifiers on the reviews and 162 present a visual summary of aspects in the reviews. The proposed method outperforms the previous studies in terms 164 of accuracy. Aye and Aung [24] perform SA on the Myanmar 165 language using a lexicon-based approach using a dataset of 166 500 restaurant reviews collected from OSNs. The authors 167 develop a lexicon using a dictionary-based approach. They 168 identify sentiment targets and assign polarity to the respective 169 sentiments of the targets. The identification of aspect terms 170 is context-independent. The performance of the automatic 171 polarity extraction method is compared with manually anno-172 tated reviews. Results of the proposed technique show high 173 accuracy. 174 Alqaryouti et al. [25] perform aspect-based SA using lexi-175 con and rule-based approaches in the Arabic language. The 176 authors discuss the aspect categories based on the stan-177 dards provided by mobile companies. They extract implicit 178 and explicit aspects from the lexicons, match the opinion 179 words and aspects with the lexicon to find the category of 180 the aspects, and assign the polarity of the opinion words 181 using lexicons based on devised rules. The proposed method 182 achieves an accuracy of 93%. Ibrahim et al. [26] develop a 183 lexicon to perform the SA of idioms in the Arabic lan-184 guage. The authors collect data manually through APIs con-185 sisting of proverbs, idioms, phrases, etc., and annotate it 186 through different annotators. The developed lexicon consists 187 of four columns, i.e., proverbs/idioms, English translation of 188 proverbs, Buck Walter, and polarity. They detect the proverbs 189 using bi-grams to six-grams and similarity between two texts 190 is determined using cosine similarity and the Levenshtein 191 distance algorithm. In the extraction phase, heuristic rules are 192 used to classify sentiments to avoid redundancy. To detect 193 polarity, positive proverbs are replaced by positive phrases, 194 and negative proverbs are replaced by negative phrases. The 195 results show an accuracy of 81.60% when the n-gram model 196 is used with cosine similarity, an accuracy of 86.12% when 197 n-gram is used with the edit distance algorithm, and an 198 accuracy of 98.62% when n-gram, cosine similarity, and edit 199 distance are used collectively. 200 B. MACHINE LEARNING APPROACH 201 Mehmood et al. [18] propose a novel feature spamming 202 approach to assign weights to terms in Roman Urdu which 203 helps to extract the most relevant and useful information 204 from data. Firstly, they identify important features using term 205 utility criteria (used to assign higher scores to significant 206 topics and vice versa). Furthermore, these distinctive features 207 are spammed using spamming factor which is adjusted by 208 user-defined hyperparameters while the weights of all other 209
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