Survey of Trending Techniques for Detection of Emerging Topics in Computer Science within Social Media

With the advent of Internet there has been a significant and exponential growth in information available to users. The availability of resources like smart mobile phone, low cost data plans and improvement in mobile communication infrastructure has further increased the reach and availability of information. The Internet allowed creation of websites and applications that significantly kept on adding data. The data generated through these websites can be structured (relational database), unstructured (digital images, video, audio files) or semi-structured (word document). The growth of Internet and WWW services gave user a liberty to create his own data, which then can be shared with the world. The development of User-Generated Content (UGC) [2] such as blogs, wikis, forums, tweets, discussions, posts, chats, podcasts, advertisements and other form of media led to the shift of information exchange from media conglomerates to individual user. With this huge amount of data, we address the problem of trending the emerging topics. Identify trending of these emerging topics allows us to know the probable trend of computer science research topics or other relevant research topics in future

Hybrid approach: naive bayes and sentiment VADER for analyzing sentiment of mobile unboxing video comments

Revolution in social media has attracted the users towards video sharing sites like YouTube. It is the most popular social media site where people view, share and interact by commenting on the videos. There are various types of videos that are shared by the users like songs, movie trailers, news, entertainment etc. Nowadays the most trending videos is the unboxing videos and in particular unboxing of mobile phones which gets more views, likes/dislikes and comments. Analyzing the comments of the mobile unboxing videos provides the opinion of the viewers towards the mobile phone. Studying the sentiment expressed in these comments show if the mobile phone is getting positive or negative feedback. A Hybrid approach combining the lexicon approach Sentiment VADER and machine learning algorithm Naive Bayes is applied on the comments to predict the sentiment. Sentiment VADER has a good impact on the Naive Bayes classifier in predicting the sentiment of the comment. The classifier achieves an accuracy of 79.78% and F1 score of 83.72%

Classifying YouTube Comments Based on Sentiment and Type of Sentence

As a YouTube channel grows, each video can potentially collect enormous amounts of comments that provide direct feedback from the viewers. These comments are a major means of understanding viewer expectations and improving channel engagement. However, the comments only represent a general collection of user opinions about the channel and the content. Many comments are poorly constructed, trivial, and have improper spellings and grammatical errors. As a result, it is a tedious job to identify the comments that best interest the content creators. In this paper, we extract and classify the raw comments into different categories based on both sentiment and sentence types that will help YouTubers find relevant comments for growing their viewership. Existing studies have focused either on sentiment analysis (positive and negative) or classification of sub-types within the same sentence types (e.g., types of questions) on a text corpus. These have limited application on non-traditional text corpus like YouTube comments. We address this challenge of text extraction and classification from YouTube comments using well-known statistical measures and machine learning models. We evaluate each combination of statistical measure and the machine learning model using cross validation and $F_1$ scores. The results show that our approach that incorporates conventional methods performs well on the classification task, validating its potential in assisting content creators increase viewer engagement on their channel.Comment: This paper was accepted at 2021 International Conference on Knowledge Discovery and Machine Learning (KDML 2021), but later withdrawn. The paper should be taken as a non peer-reviewed publicatio

CMFRI Annual Report 2018-19

CMFRI had 37 in-house research projects, 34 externally funded projects and 12 consultancy projects in operation in the year 2018-19. Total marine fish landings along the coast of mainland of India for the year 2018 is estimated at 3.49 million tonnes showing a decline of about 3.47 lakh tonnes (9%) compared to 3.83 million tonnes in 2107. Among the nine maritime states Gujarat remained in the first position with landings of 7.80 lakh tonnes followed by Tamil Nadu with 7.02 lakh tonnes. Indian oil sardine, the topmost contributor to the Indian marine fish basket recorded the sharpest fall of 54%, plummeting to ninth position from its first position in 2017. Indian mackerel became the topmost resource with a contribution on 2.84 lakh tonnes towards the total landings (8.1%). Sustained bumper landings of red toothed triggerfish (Odonus niger) were observed in the west coast since August 2018. There was considerable reduction in the number of fishing days in West Bengal, Odisha, Andhra Pradesh, Tamil Nadu and SummaryPuducherry due to cyclonic storms Titli, Gaja and Phethai. The assemblage wise marine fish landings of Gujarat for the year 2018 showed the predominance of molluscan resources (7%). Pelagic finfish resources (38%), followed by demersal (30%), crustaceans (25%) and molluscan resources (7%). The marine fish landings in Maharashtra during 2018 was 2.95 lakh t with 22.5% decrease from previous year (3.81 lakh t in 2017). The prominent species/groups that contributed to the fishery of the state were non-penaeid shrimps (12.6%), penaeid shrimps (11.4%), croakers (10.2%), threadfin breams (8.4%), Indian mackerel (7.1%), Bombay duck (5.6%) and squids (5.2%). Marine fish landings in Kerala during 2018 were 6.42 lakh t which was 9.8% higher than that of the previous year (2017). The major resources in the catch was Indian mackerel (12.6%) followed by oil sardine (12%), threadfin breams (8.3%), Stolephorus (8%) and penaeid shrimps (7.9%). Pelagic finfishes dominated the landings with a share of 62%, which was 6.1% higher than that of the previous year’s estimated pelagic catch. The total marine landing in Tamil Nadu in 2018 was 7.02 lakh t showing an increase of 7% when compared to previous year. Pelagic finfishes formed 52.1%, demersal fin fishes 33%, crustaceans and cephalopod 7.5% each. The total landing in Puducherry was 45406 t showing an increase of 68% when compared to previous year. Pelagic resources formed 30.5%, demersal 27.2%, crustaceans 17.7% and cephalopods 22.2%. Marine landings of Andhra Pradesh were 1.92 lakh t in 2018. There was a decline of 3.6% in marine landings of the state from 2018 to 2017. The marine landings of the state have been in constant decline since the peak landings of 2014. Pelagic fishes were the dominant resource followed by demersal, crustaceans and molluscans. Lesser sardines dominated by weight accounting for 17.8% of the total fish landed. Among pelagics, major resources landed were clupeids (47.7%), mackerel (13.84%), carangids (12.4%), ribbonfish (7.25%), tunas (6.3%) and seerfish (3.15%). Barracuda and billfish contributed 2.49% and 1.6%, respectively. The major demersal resources were croakers (17.8%), other perches (10.2%), goatfish (9.9%), threadfin breams (8.9%) and catfish (8.6%). Crustacean landing was contributed by penaeid shrimps (68.9%), non-penaeid shrimps (2.8%), crabs (27.4%), lobsters (0.2%) and stomatopods (0.7%). The major molluscan resources were the cephalopods which comprised of the cuttlefishes (76.44%) and squids (23.56%). West Bengal during 2018 was 1.6 lakh t which decreased by about 56% compared to the previous year (3.6 lakh t). The total marine landings of Odisha coast during 2018 was estimated at 89178 t registering a decline of about 30% compared to the previous year (126958 t). Large pelagic fish landing during 2018 was only 249,876 t by registering an improvement of about 22% over the previous landing. Major share of the landing was constituted by tunas, followed by barracudas, seerfishes and billfishes. Among the maritime states Tamil Nadu is the major contributor, followed by Kerala, Gujarat and Karnataka. Elasmobranch landings in India during 2018 was 42,117 t, increasing marginally by 2% from the previous year. Tamil Nadu and Gujarat were the major contributors. The west coast accounted for 50.5% of the landings and the east coast, 49.5%. Tamil Nadu, Puducherry, Gujarat and Daman and Diu together accounted for 68.4% of the total elasmobranch landings in the country. Bivalve production in 2018 in the country was estimated at 1,32,531 tonnes. The fishery was dominated by clams, consisting of 76.3%, followed by mussels, 15.3% and oysters, 8.4%. Clams dominated the fishery contributing 76.3% to the annual bivalve production followed by mussels, 15.3% and oysters, 8.4%. Gastropod fisheries assessment and developments in shell craft industry was also a part of the molluscan research

Video Analysis for Understanding Human Actions and Interactions

Each time that we act, our actions are not just conditioned by the spatial information, e.g., objects, people, and the scene where we are involved. These actions are also conditioned temporally with the previous actions that we have done. Indeed, we live in an evolving and dynamic world. To understand what a person is doing, we reason jointly over spatial and temporal information. Intelligent systems that interact with people and perform useful tasks will also require this ability. In light of this need, video analysis has become, in recent years, an essential field in computer vision, providing to the community a wide range of tasks to solve. In this thesis, we make several contributions to the literature of video analysis, exploring different tasks that aim to understand human actions and interactions. We begin by considering the challenging problem of human action anticipation. In this task, we seek to predict a person's action as early as possible before it is completed. This task is critical for applications where machines have to react to human actions. We introduce a novel approach that forecasts the most plausible future human motion by hallucinating motion representations. Then, we address the challenging problem of temporal moment localization. It consists of finding the temporal localization of a natural-language query in a long untrimmed video. Although the queries could be anything that is happening within the video, the vast majority of them describe human actions. In contrast with the propose and rank approaches, where methods create or use predefined clips as candidates, we introduce a proposal-free approach that localizes the query by looking at the whole video at once. We also consider the temporal annotations' subjectivity and propose a soft-labelling using a categorical distribution centred on the annotated start and end. Equipped with a proposal-free architecture, we tackle the temporal moment localization introducing a spatial-temporal graph. We found that one of the limitations of the existing methods is the lack of spatial cues involved in the video and the query, i.e., objects and people. We create six semantically meaningful nodes. Three that are feed with visual features of people, objects, and activities, and the other three that capture the relationship at the language level of the "subject-object,'' "subject-verb," and "verb-object." We use a language-conditional message-passing algorithm to capture the relationship between nodes and create an improved representation of the activity. A temporal graph uses this new representation to determine the start and end of the query. Last, we study the problem of fine-grained opinion mining in video review using a multi-modal setting. There is increasing use of video as a source of information for guidance in the shopping process. People use video reviews as a guide to answering what, why, and where to buy something. We tackle this problem using the three different modalities inherently present in a video ---audio, frames, and transcripts--- to determine the most relevant aspect of the product under review and the sentiment polarity of the reviewer upon that aspect. We propose an early fusion mechanism of the three modalities. In this approach, we fuse the three different modalities at the sentence level. It is a general framework that does not lay in any strict constraints on the individual encodings of the audio, video frames and transcripts