Sentiment analysis is a classification task where polarity of textual data is identified, i.e. to analyze whether a sentence or document expresses a negative, positive or neutral sentiment. Manipuri is a less privileged, highly agglutinative and tonal language. Despite being a scheduled language of Indian Constitution, it is also a resource constrained language. In this work, we report the sentiment analysis for Manipuri using different types of machine learning based approaches. The dataset used in our work is collected from local daily newspaper. The novelty of this work is that we carry out language specific pre-processing tasks such as transliteration, building negative morpheme based lexicon and filtering of noisy words. Using them as additional linguistic features in our models improves the classification result in terms of precision, recall and F-score. The ensemble voting of best three classifiers based on TF-IDF perform better than BM25 based classifiers and other stand-alone classifiers. Based on this result, we attempt to classify the sentiment of news articles during a certain period of time. Further, we report the finding of deep learning based approaches on the same dataset.

Low resource language specific pre-processing and features for sentiment analysis task

Multimodality has shown to be helpful in several natural language processing tasks. Thus, adding multiple modalities to the traditional sentiment analysis has also proven to be useful. However, multimodality in a low resource setting for sentiment analysis is yet to be explored for several resource-constrained languages. Assamese is a low-resource language spoken mainly in the state of Assam in India. This paper presents an Assamese multimodal dataset comprising of 16,000 articles from the news domain as a benchmark resource. Secondly, we present a multi-stage multimodal sentiment analysis framework that concurrently exploits textual and visual cues to determine the sentiment. The proposed architecture encodes the news content collaboratively. The text branch encodes semantic content information by considering the semantic information of the news. At the same time, the visual branch encodes the visual appearance information from the news image. Then, an intermediate fusion-based multimodal framework is proposed to exploit the internal correlation between textual and visual features for joint sentiment classification. Finally, a decision-level fusion mechanism is employed on the three models to integrate cross-modal information effectively for final sentiment prediction. Experimental results conducted on the Assamese dataset built in-house demonstrate that the contextual integration of multimodal features delivers better performance (89.3%) than the best unimodal features (85.6%). • A multimodal and multi-purpose Assamese news dataset is presented. • Two models are proposed for visual and textual modality for sentiment classification. • A multi-stage fusion approach is employed for multimodal sentiment classification. • Qualitative and quantitative analysis are carried out of the models. 

A multi-stage multimodal framework for sentiment analysis of Assamese in low resource setting

Topic adaptive sentiment classification based community detection for social influential gauging in online social networks

Sentiment analysis has come long way since it was introduced as a natural language processing task nearly 20 years ago. Sentiment analysis aims to extract the underlying attitudes and opinions toward an entity. It has become a powerful tool used by governments, businesses, medicine, marketing etc. The traditional sentiment analysis model focuses mainly on text content. However, technological advances have allowed people to express their opinions and feelings through audio, image and video channels. As a result, sentiment analysis is shifting from unimodality to multimodality. Multimodal sentiment analysis brings new opportunities with the rapid increase of sentiment analysis as complementary data streams enable improved and deeper sentiment detection which goes beyond text-based analysis. Audio and video channels are included in multimodal sentiment analysis in terms of broadness. People have been working on different approaches to improve sentiment analysis system performance by employing complex deep neural architectures. Recently, sentiment analysis has achieved significant success using the transformer-based model. This paper presents a comprehensive study of different sentiment analysis approaches, applications, challenges and resources then concludes that it holds tremendous potential. The primary motivation of this survey is to highlight changing trends in the unimodality to multimodality for solving sentiment analysis tasks.

https://dl.acm.org/doi/pdf/10.1145/3586075

Multimodal Sentiment Analysis: A Survey of Methods, Trends and Challenges

Unsupervised tweets categorization using semantic and statistical features

Sentiment analysis is a novel approach towards analyzing the sentiment of a given text or document. In particular, the sentiment analysis of short texts such as single sentences and social media tweets and comments are one of the main challenges of natural language processing (NLP) given the limited contextual information they usually convey. In recent years, deep learning application has shown promising result in natural language processing. This paper presents the comparative analysis of various deep learning, traditional machine learning and lexicon based methodologies of sentiment analysis. We present the importance of pre-processing and feature engineering for sentiment analysis on a resource constrained data-set of Manipuri comments gathered from social media platform using these approaches.

Review Comments of Manipuri Online Video: Good, Bad or Ugly

There is no or little work on natural language processing of Tangkhul language The current work is a humble beginning of morphological processing of this language using an unsupervised approach We use a small corpus collected from different sources of text books, short stories and articles of other topics Based on the experiments carried out, the morpheme identification task using morphessor gives reasonable and interesting output despite using a small corpus

Mirinso Shadang

Papers

Review Comments of Manipuri Online Video: Good, Bad or Ugly

Towards the Study of Morphological Processing of the Tangkhul Language.