Sentiment Analysis in Hindi—A Survey on the State-of-the-art Techniques

Sentiment Analysis (SA) has been a core interest in the field of text mining research, dealing with computational processing of sentiments, views, and subjective nature of the text. Due to the availability of extensive web-based data in Indian languages such as Hindi, Marathi, Kannada, Tamil, and so on. It has become extremely significant to analyze this data and recover valuable and relevant information. Hindi being the first language of the majority of the population in India, SA in Hindi has turned out to be a critical task particularly for companies and government organizations. This research portrays a systematic review specifically in the field of Hindi SA. The major contribution of this article includes the categorization of numerous articles based on techniques that have attracted researchers in performing SA tasks in Hindi language. This survey classifies these state-of-the-art computational intelligence techniques into four major categories namely lexicon-based techniques, machine learning techniques, deep learning techniques, and hybrid techniques. It discusses the importance of these techniques based on different aspects such as their impact on the issues of SA, levels of analysis, and performance evaluation measures. The research puts forward a comprehensive overview of the majority of the work done in Hindi SA. This study will help researchers in finding out resources such as annotated datasets, linguistic resources, and lexical resources. This survey delivers some significant findings and presents overall future research directions in the field of Hindi SA.

Digital Dose of Didactics: Reinforcing Patriarchy through Moral Stories on YouTube

Should girls get a formal education? Should women earn? And who should handle the money they earn, if at all? Can a woman’s personality be tied to learning and earning? These questions may be easily overlooked in the 21st century, when women have forayed into almost all possible careers. However, these and more questions related to women’s education, employment and empowerment find clear answers in the so-called moral stories in Hindi and other Indian languages, one finds on YouTube these days. The paper analyzes a selection of such stories centered on women and argues how these ‘moral stories’ ultimately emerge as schemes to keep the patriarchal structure alive by creating an easily accessible digital repository, and end up patronizing women in the pretext of empowering them. The paper focuses particularly on the idea of ‘moral’ these stories contain and argues that the moral messages they convey are actually detrimental to the empowerment of women as their deep structures work to cement the foundations of patriarchy.

Semantics of classifiers in some Indian Languages

In the present paper, we present a detailed description of the classifier systems of five Indian languages-- Mizo, Galo, Tagin (all belongs to the Tibeto-Burman family), Assamese (Indo-Aryan) and Malto (Dravidian). It is observed that the classifiers are a predominant feature in the Tibeto-Burman and we observe an extensive classifier system in these languages. There is no equivalent classifier system in other language families. However in the languages belonging to Eastern India, irrespective of the family, there is some sort of classifier system. Thus classifiers seem to be an areal feature in most of the Eastern and whole of the North-Eastern India. The purpose of the paper is to study if there is some semantic similarity among the classifier systems across language families in this area and thus to see if it is indeed an areal feature. It is just a preliminary description of an ongoing research in which we intend to study many more languages and include languages from the Austro-Asiatic family (such as Khasi and Munda languages spoken in Jharkhand) as well.

Recognizing Ancient South Indian Language Using Artificial Neural Network Associate With Opposition Based Grey Wolf Optimization Algorithm

Recognizing signs and fonts of prehistoric language is a fairly difficult job that require special tools. This stipulation makes the dispensation period overriding, difficult, and tiresome to calculate. This paper presents a technique for recognizing ancient south Indian languages by applying Artificial Neural Network (ANN) associated with Opposition based Grey Wolf Optimization Algorithm (OGWA). It identifies the prehistoric language, signs and fonts. It is apparent from the ANN system that arbitrarily produced weights or neurons linking various layers plays a significant role in its performance. For adaptively determining these weights, this paper applies various optimization algorithms such as Opposition based Grey Wolf Optimization, Particle Swarm Optimization and Grey Wolf Optimization to the ANN system. Performance results have illustrated that the proposed ANN-OGWO technique achieves superior accuracy over the other techniques. In test case 1, the accuracy value of OGWO is 94.89% and in test case 2, the accuracy value of OGWO is 92.34%, on average, the accuracy of OGWO achieves 5.8% greater accuracy than ANN-GWO, 10.1% greater accuracy than ANN-PSO and 22.1% greater accuracy over conventional ANN technique.

Long-Term Average Speech Spectra and Dynamic Ranges of 17 Indian Languages

Purpose: In this work, we have determined the long-term average speech spectra (LTASS) and dynamic ranges (DR) of 17 Indian languages. This work is important because LTASS and DR are language-dependent functions used to fit hearing aids, calculate the Speech Intelligibility Index, and recognize speech automatically. Currently, LTASS and DR functions for English are used to fit hearing aids in India. Our work may help improve the performance of hearing aids in the Indian context. Method: Speech samples from native talkers were used as stimuli in this study. Each speech sample was initially cleaned for extraneous sounds and excessively long pauses. Next, LTASS and DR functions for each language were calculated for different frequency bands. Similar analysis was also performed for English for reference purposes. Two-way analysis of variance was also conducted to understand the effects of important parameters on LTASS and DR. Finally, a one-sample t test was conducted to assess the significance of important statistical attributes of our data. Results: We showed that LTASS and DR for Indian languages are 5–10 dB and 11 dB less than those for English. These differences may be due to lesser use rate of high-frequency dominant phonemes and preponderance of vowel-ending words in Indian languages. We also showed that LTASS and DR do not differ significantly across Indian languages. Hence, we propose a common LTASS and DR for Indian languages. Conclusions: We showed that differences in LTASS and DR for Indian languages vis-à-vis English are large and significant. Such differences may be attributed to phonetic and linguistic characteristics of Indian languages.