{"title":"TWITTER SENTIMENT ANALYSIS","authors":"Vedurumudi Priyanka, June","doi":"10.26634/jcom.8.4.18269","DOIUrl":"https://doi.org/10.26634/jcom.8.4.18269","url":null,"abstract":"In this report, address the problem of sentiment classi�cation on twitter dataset. used a number of machine learning and deep learning methods to perform sentiment analysis. In the end, used a majority vote ensemble method with 5 of our best models to achieve the classi�cation accuracy of 83.58% on kaggle public leaderboard.","PeriodicalId":130578,"journal":{"name":"i-manager's Journal on Computer Science","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120953615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"SVM-Based Stock Market Price Prediction Methods: An Advanced Review","authors":"Kumar Vishwakarma Vijay, P. Narayan","doi":"10.26634/jcom.10.3.19183","DOIUrl":"https://doi.org/10.26634/jcom.10.3.19183","url":null,"abstract":"This paper offers a concise analysis of the strategies currently in use for stock price prediction by retail investors. The price may increase or decrease according to the quarterly results, financial news flow, technical behavior, or market sentiment resulting from recent developments worldwide. This paper discussed the accuracy of many proposed approaches and methodologies for predicting stock price movement. The Support Vector Machine (SVM) is the foundation of the approaches, with additional parameters and variables.","PeriodicalId":130578,"journal":{"name":"i-manager's Journal on Computer Science","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122662615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Techniques for Detecting Video Shot Boundaries: A Review","authors":"S. Sharma, P. Malakar","doi":"10.26634/jcom.10.3.19111","DOIUrl":"https://doi.org/10.26634/jcom.10.3.19111","url":null,"abstract":"Due to the Corona Virus Diseases (COVID-19) pandemic, education is completely dependent on digital platforms, so recent advances in technology have made a tremendous amount of video content available. Due to the huge amount of video content, content-based information retrieval has become more and more important. Video content retrieval, just like information retrieval, requires some pre-processing such as indexing, key frame selection, and, most importantly, accurate detection of video shots. This gives the way for video information to be stored in a manner that will allow easy access. Video processing plays a vital role in many large applications. The applications required to perform the various manipulations on video streams (as on frames or say shots). The high definition of video can take a lot of memory to store, so compression techniques are huge in demand. Also, object tracking or object identification is an area where much considerable research has taken place and it is in progress.","PeriodicalId":130578,"journal":{"name":"i-manager's Journal on Computer Science","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129875943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"SWITCHING AMONG DIFFERENT CLUSTER\u0000SIZES USING 'C' LANGUAGE","authors":"P. Rajesh","doi":"10.26634/jcom.9.2.14594","DOIUrl":"https://doi.org/10.26634/jcom.9.2.14594","url":null,"abstract":"","PeriodicalId":130578,"journal":{"name":"i-manager's Journal on Computer Science","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129645606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}