Vishwanath R Hulipalled

Bio: Vishwanath R Hulipalled is an academic researcher from Reva Institute of Technology and Management. The author has contributed to research in topics: Computer science & Artificial intelligence. The author has an hindex of 3, co-authored 11 publications receiving 54 citations.

Every post matters: A survey on applications of sentiment analysis in social media

Abstract: Social media is often used by people to express their views about everything around them. Twitter is considered as a major microblogging site where each post by the user is publicly available. Due to the public availability, it makes Twitter the most popular social media for analysis of opinions of the masses for various purposes. These purposes range from predicting an election winner to identifying the polarity of people towards a particular brand. This paper provides an introduction to Sentiment Analysis and the importance of pre-processing of texts in social media. With a detailed description of how performance evaluation is done, it looks into research works which make use of Twitter data in order to obtain insightful details in various domains and help identify the trending research areas within Social Media Analysis.

Spamming the mainstream: A survey on trending Twitter spam detection techniques

Abstract: In recent years, social networking sites are being referred frequently by the people, due to this the social networking sites are growing very fast. Twitter is one such micro-blogging site where the users are able to connect with new people and know what is happening in the world through the topics discussed on twitter. For this reason, twitter is targeted by malicious users who post harmful links, unwanted messages which are not of users interest which is called, spam. In this paper, a comprehensive survey on existing methods on twitter spam detection is presented. From this survey, it is clear that detecting URL content in the tweet is very important to know whether the tweet is spam or non-spam. The advantages and faws are discussed. The comparative analysis of the existing detection methods are also presented by reviewing research papers published from 2010–2017. There is a lot of scope for the researchers to carry out their research in fnding out an eff cient Twitter Spam Detection method.

SAEKCS: Sentiment Analysis for English – Kannada Code SwitchText Using Deep Learning Techniques

Abstract: Usage of social media has become more widespread to express sentiment, emotion about public events, government policies, product reviews etc. Performing Sentiment Analysis (SA) on social media data will give more and more insights about user’s behavior. Multilingual society like India, it is very common to use code switch text in social media to express their views. Switching between languages while communicating is refer as code mixing or code switching. Analyzing this code switch text and getting the useful information from this too harder because of its unstructured linguistic nature. In this paper, we proposed a hybrid model called SAEKCS for sentiment analysis on Kannada-English code switch text. Our proposed model uses deep learning techniques like Convolutional Neural Network (CNN) and Bidirectional Long Short Term Memory (BiLSTM) for sentiment analysis in code switch text. Our experimental results shows that 77.6% of accuracy and 69.6% of coverage. These results are much better than existing works [17] [18].

A Study On Agriculture Commodities Price Prediction and Forecasting

Abstract: Recent days interaction between computer and human is gaining more popularity or momentum, especially in the area of speech recognition. There are many speech recognition systems or applications got developed such as, Amazon Alexa, Cortana, Siri etc. To provide the human like responses, Natural Language Processing techniques such as Natural Language Toolkit [6] for Python can be used for analyzing speech, and responses. In our country, INDIA, agriculture is backbone of economy and major contributor for GDP. However, farmers often, do not get sufficient support or required information in the regional languages. Prediction analysis for farmers in agriculture is not only for crop growing but is essential to develop Crop recommendation system based on price forecasting for agricultural commodities in addition to providing useful advisories for the farmers of any state. Currently, to protect the farmers from price crash or control the inflation, the governments (Central and State) predicting the price for agricultural commodities using short-term arrivals and historical data. However, these methods are not giving enough recommendations for the farmers to decide the storage/sales options with evidence-based explanations. The goal of this study is to identify the research already done in this area and find out the pros and cons of different models and future scope for improvement

A survey on classification techniques for opinion mining and sentiment analysis

Abstract: Opinion mining is considered as a subfield of natural language processing, information retrieval and text mining. Opinion mining is the process of extracting human thoughts and perceptions from unstructured texts, which with regard to the emergence of online social media and mass volume of users’ comments, has become to a useful, attractive and also challenging issue. There are varieties of researches with different trends and approaches in this area, but the lack of a comprehensive study to investigate them from all aspects is tangible. In this paper we represent a complete, multilateral and systematic review of opinion mining and sentiment analysis to classify available methods and compare their advantages and drawbacks, in order to have better understanding of available challenges and solutions to clarify the future direction. For this purpose, we present a proper framework of opinion mining accompanying with its steps and levels and then we completely monitor, classify, summarize and compare proposed techniques for aspect extraction, opinion classification, summary production and evaluation, based on the major validated scientific works. In order to have a better comparison, we also propose some factors in each category, which help to have a better understanding of advantages and disadvantages of different methods.

A Systematic Review of Emoji: Current Research and Future Perspectives.

Abstract: A growing body of research explores emoji, which are visual symbols in computer mediated communication (CMC). In the 20 years since the first set of emoji was released, research on it has been on the increase, albeit in a variety of directions. We reviewed the extant body of research on emoji and noted the development, usage, function, and application of emoji. In this review article, we provide a systematic review of the extant body of work on emoji, reviewing how they have developed, how they are used differently, what functions they have and what research has been conducted on them in different domains. Furthermore, we summarize directions for future research on this topic.

New Modality: Emoji Challenges in Prediction, Anticipation, and Retrieval

Abstract: Over the past decade, emoji have emerged as a new and widespread form of digital communication, spanning diverse social networks and spoken languages. We propose treating these ideograms as a new modality in their own right, distinct in their semantic structure from both the text in which they are often embedded as well as the images which they resemble. As a new modality, emoji present rich novel possibilities for representation and interaction. In this paper, we explore the challenges that arise naturally from considering the emoji modality through the lens of multimedia research, specifically the ways in which emoji can be related to other common modalities such as text and images. To do so, we first present a large-scale data set of real-world emoji usage collected from Twitter. This data set contains examples of both text-emoji and image-emoji relationships within tweets. We present baseline results on the challenge of predicting emoji from both text and images, using state-of-the-art neural networks. Further, we offer a first consideration into the problem of how to account for new, unseen emoji—a relevant issue as the emoji vocabulary continues to expand on a yearly basis. Finally, we present results for multimedia retrieval using emoji as queries.