A Comprehensive Review of Sentiment Analysis on Indian Regional Languages: Techniques, Challenges, and Trends

Sentiment analysis (SA) is the process of understanding emotion within a text. It helps identify the opinion, attitude, and tone of a text categorizing it into positive, negative, or neutral. SA is frequently used today as more and more people get a chance to put out their thoughts due to the advent of social media. Sentiment analysis benefits industries around the globe, like finance, advertising, marketing, travel, hospitality, etc. Although the majority of work done in this field is on global languages like English, in recent years, the importance of SA in local languages has also been widely recognized. This has led to considerable research in the analysis of Indian regional languages. This paper comprehensively reviews SA in the following major Indian Regional languages: Marathi, Hindi, Tamil, Telugu, Malayalam, Bengali, Gujarati, and Urdu. Furthermore, this paper presents techniques, challenges, findings, recent research trends, and future scope for enhancing results accuracy

Generating Javanese Stopwords List using K-means Clustering Algorithm

Stopword removal necessary in Information Retrieval. It can remove frequently appeared and general words to reduce memory storage. The algorithm eliminates each word that is precisely the same as the word in the stopword list. However, generating the list could be time-consuming. The words in a specific language and domain must be collected and validated by specialists. This research aims to develop a new way to generate a stop word list using the K-means Clustering method. The proposed approach groups words based on their frequency. The confusion matrix calculates the difference between the findings with a valid stopword list created by a Javanese linguist. The accuracy of the proposed method is 78.28% (K=7). The result shows that the generation of Javanese stopword lists using a clustering method is reliable

Purifying the nation : the Arya Samaj in Gujarat 1895-1930

This article examines the impact of the Arya Samaj in Gujarat from 1895 to 1930. Although the founder of this body, Dayanand Saraswati, was from Gujarat, it proved less popular there initially than in the Punjab. The first important Arya Samajists in Gujarat were Punjabis, brought there by Sayajirao Gaekwad of Baroda to carry out educational work amongst untouchables. The Arya Samaj only became a mass organisation in Gujarat after a wave of conversions to Christianity in central Gujarat by untouchables, with Arya Samajists starting orphanages to ‘save’ orphans from the clutches of the Christian missionaries. The movement then made considerable headway in Gujarat. The main followers were from the urban middle classes, higher farming castes, and gentry of the Koli caste. Each had their own reasons for embracing the organisation, ranging from a desire for higher social status, to religious reform, to building caste unity, and as a means, in the case of the Koli gentry, to ‘reconvert’ Kolis who had adopted Islam in medieval times. The movement lost its momentum after Gandhi arrived on the political scene, and many erstwhile Arya Samajists embraced the Gandhian movement. When the Gandhian movement itself flagged after 1922, there was an upsurge in communal antagonism in Gujarat in which Arya Samajists played a provocative role. A riot in Godhra in 1928 is examined. Over the past decade, Gujarat has come to be seen as a hotbed of communalism, ruled by a state government that has connived at, and even encouraged, murderous attacks on Muslims and Christians. At the time of the notorious pogrom against Muslims of 2002, several observers commented on the irony that this should have occurred in the homeland of Gandhi, the great proponent of non-violence and Hindu-Muslim unity.1 They saw this as violating not only the memory of the Mahatma, but also the very history of this region – one known, it was said, for its spirit of tolerance and regard for the sacredness of all life. As Tridip Suhrud stated in anguish: What has happened to the dialogic space that Gandhi nurtured? What has happened to the Jain ethos, which informed the structure of mercantile capitalism and from which Gandhi drew sustenance?2 Although these are questions that we should certainly ask, they project only one view of Gujarat and its history, for this is not an area that has escaped violence, bigotry and communal strife in the past. Communal tension between Hindus and Muslims, and even violence between the two, has a genealogy that stretches back well over a century; predating Gandhi’s arrival on the political scene in 1915.3 In this article, I shall examine an aspect of this history by focusing on the growth and development of the Arya Samaj in Gujarat between the years 1895 and 1930. It is not suggested that there was an inevitable progress from the doctrines and activities propagated by this body to the Hindu bigotry that dominates the political scene in modern Gujarat, for there were many countervailing forces at both a popular and elite level that might have produced a different trajectory.4 Also, many of the features of the modern manifestation of Hindutva were not present in the early decades of the twentieth century. Nonetheless, a way of thinking about the modern nation state and the place of Hindus and Hinduism within it became a part of the public culture of this region, and this could be deployed in new ways, and to new effect, in changing political circumstances.5 1 For example Panikkar, K. N. ‘The Agony of Gujarat,’ The Hindu, 19 March 2002; Suhrud, T. ‘Gujarat: No Room for Dialogue,’ Economic and Political Weekly, [Hereafter EPW], 37 (11), 16 March 2002, pp. 1011-12. 2 Ibid, p.1011. 3 To take one case, there was a long history of tension between Hindus and Muslims in Somnath in Kathiawad in the later nineteenth century that led to a fracas in 1892, followed by a riot in which several died in 1893. See file on ‘Patan Riot: Hindus and Mussulmans Patan Commission, Part I,’ Oriental and India Office Collection, R/2/721/56; Krishnaswamy, S. ‘A Riot in Bombay, August 11, 1893: A Study in Hindu-Muslim Relations in Western India during the Late Nineteenth Century,’ unpublished Ph.D. thesis, University of Chicago, 1966, pp. 76-90. 4 As pointed out for India as a whole by Fischer-Tiné, H., ‘Kindly Elders of the Hindu Biradri’: The Arya Samaj’s Struggle for Influence and its Effects on Hindu-Muslim Relations, 1800-1925,’ in Copley, A. (ed.), Gurus and their Followers: New Religious Reform Movements in India, New Delhi, 2000, pp.107-08. 5 In the ways alluded to for Bengal by Sarkar, S. ‘Intimations of Hindutva: Ideologies, Caste, and Class in Post-Swadeshi Bengal,’ in Sarkar, S. Beyond Nationalist Frames: Postmodernism, Hindu Fundamentalism, History, New Delhi, 2002, pp.81-95; and for the United Provinces by Gould, W. Hindu Nationalism and the Language of Politics in Late Colonial India, Cambridge 2004, p. 37

A Comparative Study of English and Gujarati Phonological Systems

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Opinion-Mining on Marglish and Devanagari Comments of YouTube Cookery Channels Using Parametric and Non-Parametric Learning Models

YouTube is a boon, and through it people can educate, entertain, and express themselves about various topics. YouTube India currently has millions of active users. As there are millions of active users it can be understood that the data present on the YouTube will be large. With India being a very diverse country, many people are multilingual. People express their opinions in a code-mix form. Code-mix form is the mixing of two or more languages. It has become a necessity to perform Sentiment Analysis on the code-mix languages as there is not much research on Indian code-mix language data. In this paper, Sentiment Analysis (SA) is carried out on the Marglish (Marathi + English) as well as Devanagari Marathi comments which are extracted from the YouTube API from top Marathi channels. Several machine-learning models are applied on the dataset along with 3 different vectorizing techniques. Multilayer Perceptron (MLP) with Count vectorizer provides the best accuracy of 62.68% on the Marglish dataset and Bernoulli Naïve Bayes along with the Count vectorizer, which gives accuracy of 60.60% on the Devanagari dataset. Multilayer Perceptron and Bernoulli Naïve Bayes are considered to be the best performing algorithms. 10-fold cross-validation and statistical testing was also carried out on the dataset to confirm the results