Abstract
The current chapter focus on providing a comparative study for identifying sentiment of Malayalam tweets using deep learning methods such as convolutional neural net (CNN), long short-term memory units (LSTM). The baseline methods used to compare are support vector machines (SVM), regularized least square classification with random kitchen sink mapping (RKS-RLSC). Malayalam is a low resource language spoken in Kerala state, India. Due to the unavailability of data, tweets were collected and labeled manually based on its polarity as neutral, negative and positive. RKS mapping is a well explored approach in which data are nonlinearly mapped to higher dimension where linear classifier can be used. The evaluation measure chosen for the experiments are F1-score, recall, accuracy and precision. The experiments also provide a comparison with classical methods such as logistic regression (LR), adaboost (Ab), random forest (RF), decision tree (DT), k-nearest neighbor (KNN) on the basis of accuracy as the measure. For the experiments using CNN and LSTM, we report the effectiveness of activation functions such as rectified linear units (ReLU), exponential linear units (ELU) and scaled exponential linear units (SELU) for the sentiment identification of Malayalam tweets over SVM and RKS-RLSC.
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References
Zikopoulos P, Eaton C, DeRoos D, Deutch T, Lapis G (2011) Understanding big data: analytics for enterprise class Hadoop and streaming data. McGraw-Hill Osborne Media
Agarwal A, Xie B, Vovsha I, Rambow O, Passonneau R (2011) Sentiment analysis of twitter data. In: Proceedings of ACL 2011 workshop on languages in social media, pp 30–38
Saif H, He Y, Alani H (2011) Semantic smoothing for twitter sentiment analysis. In: Proceeding of the 10th international semantic web conference (ISWC)
Kiritchenko S, Zhu X, Mohammad SM (2014) Sentiment analysis of short informal texts. J Artif Intell Res 723–762
Das A, Bandyopadhyay S (2010) SentiWordNet for Indian languages. In: Proceedings of the 8th workshop on Asian Language Resources (ALR), August, pp 56–63
Maite T, Julian B, Milan T, Kimberly V, Manfred S (2011) Lexiconbased methods for sentiment analysis. Comput Linguist 37(2):267–307
Willyan DA., de Leandro NC (2014) A keyword extraction method from twitter messages represented as graphs
Saima A, Stan S (2007) Identifying expressions of emotion in text. In: Text, speech and dialogue. Springer, Berlin, Heidelberg, pp 196–205
Changhua Y, Lin KH-Y, Chen H-H (2007) Building emotion lexicon from weblog corpora. In: Proceedings of the 45th annual meeting of the ACL on interactive poster and demonstration sessions. Association for Computational Linguistics, pp 133–136
Hiroya T, Takashi I, Manabu O (2005) Extracting semantic orientations of words using spin model. In: Proceedings of the 43rd annual meeting of the association for computational linguistics (ACL’05), pp 133–140
Stefano B, Andrea E, Fabrizio S (2010) Sentiwordnet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining. In: Proceedings of the 7th conference on international language resources and evaluation (LREC’10), Valletta, Malta, May
Theresa W, Janyce W, Paul H (2005) Recognizing contextual polarity in phrase-level sentiment analysis. In: Proceedings of the HLT/EMNLP, Vancouver, Canada
Maite T, Anthony C, Voll K (2006) Creating semantic orientation dictionaries. In: Proceedings of the 5th international conference on language resources and evaluation (LREC), Genoa, pp 427–432
Yoshimitsu T, Dipankar D, Sivaji B, Manabu O (2011) Proceedings of 2nd workshop on computational approaches to subjectivity and sentimental analysis, ACL-HLT, pp 80–86
Pang B, Lee L, Vaithyanathan S (2002) Thumbs up? Sentiment classification using machine learning techniques. In: Proceedings of EMNLP, pp 79–86
Baccianella S, Esuli A, Sebastiani F (2010) SENTIWORDNET 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In: Proceedings of LREC-10
Wiebe J, Mihalcea R (2006) Word sense and subjectivity. In: Proceedings of COLING/ACL-06, pp 1065–1072
Pooja P, Sharvari G (2015) A framework for sentiment analysis in Hindi using HSWN. IJCA 119
Mittal N, Agarwal B, Chouhan G, Bania N, Prateek P (2013) Sentiment analysis of Hindi review based on negation and discourse relation. In: International joint conference on natural language processing, Nagoya, Japan, Oct 2013
Joshi A, Balamurali AR, Bhattacharyya P (2010) A fall-back strategy for sentiment analysis in Hindi: a case study. In: Proceedings of the 8th ICON
Das A, Bandyopadhyay S (2010) SentiWordNet for Indian languages. Asian Federation for Natural Language Processing (COLING), China, pp 56–63
Sumit KG, Gunjan A (2014) Sentiment analysis in Hindi language: a survey. IJMTER
Sharma R, Nigam S, Jain R (2014) Opinion mining in Hindi language: a survey. IJFCST 4(2)
Pooja P, Sharvari G (2015) A survey of sentiment classification techniques used for Indian regional languages. IJCSA 5(2)
Selvan A, Anand Kumar M, Soman KP (2015) Sentiment analysis of Tamil movie reviews via feature frequency count. In: International conference on innovations in information, embedded and communication systems (ICIIECS 15). IEEE
Mohandas N, Nair JPS, Govindaru V (2012) Domain specific sentence level mood extraction from Malayalam text. In: Advances in Computing and Communications (ICACC)
Rahimi A, Recht B (2007) Random features for large-scale kernel machines. In: Advances in neural information processing systems
Kumar SS, Premjith B, Kumar MA, Soman KP (2015) AMRITA_CEN-NLP@ SAIL2015: Sentiment analysis in Indian language using regularized least square approach with randomized feature learning. In: Mining intelligence and knowledge exploration, Dec, pp 671–683
Bengio Y, Simard P, Frasconi P (1994) Learning long-term dependencies with gradient descent is difficult. IEEE Trans Neural Netw 5:157–166
Bengio Y, Boulanger Lewandowski N, Pascanu R (2013) Advances in optimizing recurrent networks. ICASSP
Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780
Tensorflow. https://www.tensorflow.org/get_started/get_started
Sachin Kumar S, Anand Kumar M, Soman KP (2017) Sentiment analysis of tweets in Malayalam language using long sort-term memory units and convolutional neural nets. In: MIKE 2017, Springer, pp 320–334
Tweepy. https://github.com/tweepy/tweepy
Kaggle dataset. https://www.kaggle.com/c/si650winter11/data
Arabic sentiment tweet dataset. https://archive.ics.uci.edu/ml/datasets/Twitter+Data+set+for+Arabic+Sentiment+Analysis
Twitter sentiment corpus. http://www.sananalytics.com/lab/twitter-sentiment/
Sentiment analysis in Indian languages (SAIL). http://amitavadas.com/SAIL/
Sentiment analysis for Indian languages (Code Mixed). http://www.dasdipankar.com/SAILCodeMixed.html
Chang C-C, Lin C-J (2001) LIBSVM: a library for support vector machines. http://www.csie.ntu.edu.tw/~cjlin/libsvm
Scikit learn library. http://scikit-learn.org/stable/supervisedlearning.html
Yoon K (2014) Convolutional neural networks for sentence classification. arXiv:1408.5882
Kingma DP, Ba J (2014) Adam. A method for stochastic optimization. arXiv:1412.6980
Clevert DA, Unterthiner T, Hochreiter S (2016) Fast and accurate deep network learning by exponential linear units (ELUs). In: ICLR
Klambauer G, Unterthiner T, Mayr A (2017) Self-normalizing neural networks. arXiv:1706.02515
Tacchetti A, Mallapragada PK, Santoro M, Rosasco L (2013) Gurls: a least squares library for supervised learning. J Mach Learn Res 14:3201–3205
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Sachin Kumar, S., Anand Kumar, M., Soman, K.P. (2019). Identifying Sentiment of Malayalam Tweets Using Deep Learning. In: Patnaik, S., Yang, XS., Tavana, M., Popentiu-Vlădicescu, F., Qiao, F. (eds) Digital Business. Lecture Notes on Data Engineering and Communications Technologies, vol 21. Springer, Cham. https://doi.org/10.1007/978-3-319-93940-7_16
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