JUNLP@Dravidian-CodeMix-FIRE2020: Sentiment Classification of Code-Mixed Tweets using Bi-Directional RNN and Language Tags

• URL: http://arxiv.org/abs/2010.10111v1
• Date: Tue, 20 Oct 2020 08:10:29 GMT
• Title: JUNLP@Dravidian-CodeMix-FIRE2020: Sentiment Classification of Code-Mixed Tweets using Bi-Directional RNN and Language Tags
• Authors: Sainik Kumar Mahata, Dipankar Das, Sivaji Bandyopadhyay
• Abstract summary: This paper uses bi-directional LSTMs along with language tagging to facilitate sentiment tagging of code-mixed Tamil texts extracted from social media. The presented algorithm garnered precision, recall, and F1 scores of 0.59, 0.66, and 0.58 respectively.
• Score: 14.588109573710431
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sentiment analysis has been an active area of research in the past two decades and recently, with the advent of social media, there has been an increasing demand for sentiment analysis on social media texts. Since the social media texts are not in one language and are largely code-mixed in nature, the traditional sentiment classification models fail to produce acceptable results. This paper tries to solve this very research problem and uses bi-directional LSTMs along with language tagging, to facilitate sentiment tagging of code-mixed Tamil texts that have been extracted from social media. The presented algorithm, when evaluated on the test data, garnered precision, recall, and F1 scores of 0.59, 0.66, and 0.58 respectively.

Related papers

• A General and Flexible Multi-concept Parsing Framework for Multilingual Semantic Matching [60.51839859852572]
  We propose to resolve the text into multi concepts for multilingual semantic matching to liberate the model from the reliance on NER models. We conduct comprehensive experiments on English datasets QQP and MRPC, and Chinese dataset Medical-SM.
  arXiv  Detail & Related papers  (2024-03-05T13:55:16Z)
• Unsupervised Sentiment Analysis of Plastic Surgery Social Media Posts [91.3755431537592]
  The massive collection of user posts across social media platforms is primarily untapped for artificial intelligence (AI) use cases. Natural language processing (NLP) is a subfield of AI that leverages bodies of documents, known as corpora, to train computers in human-like language understanding. This study demonstrates that the applied results of unsupervised analysis allow a computer to predict either negative, positive, or neutral user sentiment towards plastic surgery.
  arXiv  Detail & Related papers  (2023-07-05T20:16:20Z)
• Transformer-based Model for Word Level Language Identification in Code-mixed Kannada-English Texts [55.41644538483948]
  We propose the use of a Transformer based model for word-level language identification in code-mixed Kannada English texts. The proposed model on the CoLI-Kenglish dataset achieves a weighted F1-score of 0.84 and a macro F1-score of 0.61.
  arXiv  Detail & Related papers  (2022-11-26T02:39:19Z)
• IIITT@Dravidian-CodeMix-FIRE2021: Transliterate or translate? Sentiment analysis of code-mixed text in Dravidian languages [0.0]
  This research paper bestows a tiny contribution to this research in the form of sentiment analysis of code-mixed social media comments in the popular Dravidian languages Kannada, Tamil and Malayalam. It describes the work for the shared task conducted by Dravidian-CodeMix at FIRE 2021 by employing pre-trained models like ULMFiT and multilingual BERT fine-tuned on the code-mixed dataset. The results are recorded in this research paper where the best models stood 4th, 5th and 10th ranks in the Tamil, Kannada and Malayalam tasks respectively.
  arXiv  Detail & Related papers  (2021-11-15T16:57:59Z)
• Sentiment analysis in tweets: an assessment study from classical to modern text representation models [59.107260266206445]
  Short texts published on Twitter have earned significant attention as a rich source of information. Their inherent characteristics, such as the informal, and noisy linguistic style, remain challenging to many natural language processing (NLP) tasks. This study fulfils an assessment of existing language models in distinguishing the sentiment expressed in tweets by using a rich collection of 22 datasets.
  arXiv  Detail & Related papers  (2021-05-29T21:05:28Z)
• Named Entity Recognition for Social Media Texts with Semantic Augmentation [70.44281443975554]
  Existing approaches for named entity recognition suffer from data sparsity problems when conducted on short and informal texts. We propose a neural-based approach to NER for social media texts where both local (from running text) and augmented semantics are taken into account.
  arXiv  Detail & Related papers  (2020-10-29T10:06:46Z)
• NLP-CIC at SemEval-2020 Task 9: Analysing sentiment in code-switching language using a simple deep-learning classifier [63.137661897716555]
  Code-switching is a phenomenon in which two or more languages are used in the same message. We use a standard convolutional neural network model to predict the sentiment of tweets in a blend of Spanish and English languages.
  arXiv  Detail & Related papers  (2020-09-07T19:57:09Z)
• C1 at SemEval-2020 Task 9: SentiMix: Sentiment Analysis for Code-Mixed Social Media Text using Feature Engineering [0.9646922337783134]
  This paper describes our feature engineering approach to sentiment analysis in code-mixed social media text for SemEval-2020 Task 9: SentiMix. We are able to obtain a weighted F1 score of 0.65 for the "Hinglish" task and 0.63 for the "Spanglish" tasks.
  arXiv  Detail & Related papers  (2020-08-09T00:46:26Z)
• ULD@NUIG at SemEval-2020 Task 9: Generative Morphemes with an Attention Model for Sentiment Analysis in Code-Mixed Text [1.4926515182392508]
  We present the Generative Morphemes with Attention (GenMA) Model sentiment analysis system contributed to SemEval 2020 Task 9 SentiMix. The system aims to predict the sentiments of the given English-Hindi code-mixed tweets without using word-level language tags.
  arXiv  Detail & Related papers  (2020-07-27T23:58:54Z)
• BAKSA at SemEval-2020 Task 9: Bolstering CNN with Self-Attention for Sentiment Analysis of Code Mixed Text [4.456122555367167]
  We present an ensemble architecture of convolutional neural net (CNN) and self-attention based LSTM for sentiment analysis of code-mixed tweets. We achieved F1 scores of 0.707 and 0.725 on Hindi-English (Hinglish) and Spanish-English (Spanglish) datasets, respectively.
  arXiv  Detail & Related papers  (2020-07-21T14:05:51Z)
• IIT Gandhinagar at SemEval-2020 Task 9: Code-Mixed Sentiment Classification Using Candidate Sentence Generation and Selection [1.2301855531996841]
  Code-mixing adds to the challenge of analyzing the sentiment of the text due to the non-standard writing style. We present a candidate sentence generation and selection based approach on top of the Bi-LSTM based neural classifier. The proposed approach shows an improvement in the system performance as compared to the Bi-LSTM based neural classifier.
  arXiv  Detail & Related papers  (2020-06-25T14:59:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.