Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
Browse
15 results
Search Results
Item Sentiment analysis based approaches for understanding user context in Web content(2013) Kamath S․, S.S.; Bagalkotkar, A.; Khandelwal, A.; Pandey, S.; Poornima, K.In our day to day lives, we highly value the opinions of friends in making decisions about issues like which brand to buy or which movie to watch. With the increasing popularity of blogs, online reviews and social networking sites, the current trend is to look up reviews, expert opinions and discussions on the Web, so that one can make an informed decision. Sentiment analysis, also known as opinion mining is the computational study of opinions, sentiments and emotions expressed in natural language for the purpose of decision making. Sentiment analysis applies natural language processing techniques and computational linguistics to extract information about sentiments expressed by authors and readers about a particular subject, thus helping users in making sense of huge volume of unstructured Web data. Applications like review classification, product review mining and trend prediction benefit from sentiment analysis based techniques. This paper presents a study of different approaches in this field, the state of the art techniques and current research in Sentiment Analysis based approaches for understanding user's context. © 2013 IEEE.Item Automated stock price prediction and trading framework for Nifty intraday trading(2013) Bhat, A.A.; Kamath S․, S.S.Research on automated systems for Stock price prediction has gained much momentum in recent years owing to its potential to yield profits. In this paper, we present an automatic trading system for Nifty for deciding the buying and selling calls for intra-day trading that combines various methods to improve the quality and precision of the prediction. Historical data has been used to implement the various technical indicators and also to train the Neural Network that predicts movement for intra-day Nifty. Further, Sentiment Analysis techniques are applied to popular blog articles written by domain experts and to user comments to find sentiment orientation, so that analysis can be further improved and better prediction accuracy can be achieved. The system makes a prediction for every trading day with these methods to forecast if next day will be a positive day or negative. Further, buy and sell calls for intra-day trading are also decided by the system thus achieving full automation in stock trading. © 2013 IEEE.Item Cleaning and sentiment tasks for news transcript data(Springer Verlag service@springer.de, 2017) Lakshman, V.; Ananth, S.; Chhanchan, R.; Chandrasekaran, K.Today, vast amount of news in various forms is hosted on the web. They include news articles, digital newspapers, news clips, podcasts, and other sources. Traditionally, news articles and writings have been used to carry out sentiment analysis for topics. However, news channels and their transcripts represent vast data that have not been examined for business aspects. In this light, we have charted out a methodology to gather transcripts and process them for sentiment tasks by building a system to crawl Webpages for documents, index them, and aggregate them for topic analysis. Vector space model has been used for document indexing with predetermined set of topics and sentiment analysis carried out through the SentiWordNet data set, a lexical resource used for opinion mining. The areas of insight are mainly the polarity index (degree of polarity or subjectivity) of the news presented as well as their coverage. This research shows insights that can used by businesses to assess the content and quality of their content. © Springer Science+Business Media Singapore 2017.Item Domain-specific sentiment analysis approaches for code-mixed social network data(Institute of Electrical and Electronics Engineers Inc., 2017) Pravalika, A.; Oza, V.; Meghana, N.P.; Kamath S․, S.Sentiment Analysis is one of the prominent research fields in Natural Language Processing because of its widespread real-world applications. Customer preferences, options and experiences can be analyzed through social media, reviews, blogs and other online social networking site data. However, due to increasing informal usage of local languages in social media platforms, multi-lingual or code-mixed data is fast becoming a common occurrence. Mixed code is generated when users use more than a single language in social network comments. Such data presents a significant challenge for applications using sentiment analysis and is yet to be fully explored by researchers. Existing sentiment analysis methods applied to monolingual social data are not suitable for code-mixed data due to the inconsistency in the grammatical structure in these sentences. In this paper, a novel method focused on performing effective sentiment analysis of bilingual sentences written in Hindi and English is proposed, that takes into account linguistic code switching and the grammatical transitions between the two considered languages. Experimental evaluation using real-world, code-mixed datasets obtained from Facebook showed that the proposed approach achieved very good accuracy and was also efficient performance-wise. © 2017 IEEE.Item A QoS and QoE based integrated model for bidirectional web service recommendation(Institute of Electrical and Electronics Engineers Inc., 2018) Jhaveri, S.; Soundalgekar, P.M.; George, K.; Kamath S․, S.K.For a given requirement, identifying relevant Web services and recommending the best ones is an important task in Service-oriented application development. In this paper, a composite model that leverages Quality of Service (QoS) and Quality of Experience (QoE) for bidirectional Web service recommendation (bi-WSR) is proposed. The QoS based recommendation model is built on degree of user satisfaction, calculated using a special normalization technique and user satisfaction functions like response time and throughput. The QoE model is trained on a dataset containing positive, negative and neutral textual reviews of web services for sentiment analysis and mapped to each service's QoS values using a clustering method. This further optimizes the recommendation of web services to consumers, as the sentiment score of reviews is integrated with the user satisfaction using weighted average scoring. To describe the relationship between both web services consumers and providers consumers, a cube model is built. For recommending services to consumers and recommending potential consumers to service providers, hybrid collaborative filtering based techniques were used. The results obtained when only QoS is used, and when QoS and sentiment analysis scores are integrated to form QoE showed significant improvement in the quality of recommendation. © 2018 Pacific Neighborhood Consortium (PNC).Item Measuring the influence of moods on stock market using Twitter analysis(Springer Verlag service@springer.de, 2019) Cowlessur, S.K.; Annappa, B.; Sree, B.K.; Gupta, S.; Velaga, C.It is a well-known fact that sentiments play a vital role and is an incredibly influential tool in several aspects of human life. Sentiments also drive proactive business solutions. Studies have shown that the more appropriate data is gathered and analyzed at the right time, the higher the success of sentiment analysis. This paper analyses the correlation between the public mood and the variation in stock prices towards companies in different domains. For each tweet, scores are assigned to eight predefined moods namely “Joy†, “Sadness†, “Fear†, “Anger†, “Trust†, “Disgust†, “Surprise†and “Anticipation†. A regression model is applied to the mood scores and the stock prices dataset to obtain the R-squared score, which is a metric used to evaluate the model. The paper aims to find the moods that best reflect the stock values of the respective companies. From the results, it is observed that there is a definite correlation between public mood and stock market. © Springer Nature Singapore Pte Ltd. 2019.Item Predicting stock prices using ensemble learning and sentiment analysis(Institute of Electrical and Electronics Engineers Inc., 2019) Pasupulety, U.; Abdullah Anees, A.; Anmol, S.; Mohan, B.R.The recent success of the application of Artificial Intelligence in the financial sector has resulted in more firms relying on stochastic models for predicting the behaviour of the market. Everyday, quantitative analysts strive to attain better accuracies from their machine learning models for forecasting returns from stocks. Support Vector Machine (SVM) and Random Forest based regression models are known for their effectiveness in accurately predicting closing prices. In this work, we propose a technique for analyzing and predicting stock prices of companies using the aforementioned algorithms as an ensemble. Datasets from India's National Stock Exchange (NSE) containing basic market price information are preprocessed to include well known leading technical indicators as features. Feature selection, which ranks features based on their degree of influence on the final closing price has been incorporated to reduce the size of the training dataset. Additionally, we evaluate the effectiveness of considering the public opinion of a company by employing sentiment analysis. Using a trained Word2Vec model, company specific hash-tagged posts from Twitter are classified as positive or negative. Our proposed ensemble model is then trained on a new dataset which combines the technical indicator data along with the aggregated number of positive/negative tweets of a company over time. Our experiments indicate that in some scenarios, the ensemble model performs better than the constituent models and is highly dependent of the nature and size of the training data. However, combining technical indicator data with aggregated positive/negative tweet counts has a negligible effect on the performance of the ensemble model. © 2019 IEEE.Item Time series with sentiment analysis for stock price prediction(Institute of Electrical and Electronics Engineers Inc., 2019) Sharma, V.; Khemnar, R.; Kumari, R.; Mohan, B.R.Stock price prediction has been a major area of research for many years. Accurate predictions can help investors take correct decisions about the selling/purchase of stocks. This paper aims to predict and gauge stock costs and patterns, utilizing the power of machine learning, content examination and fundamental analysis, to give traders a hands-on tool for keen speculations particularly for the volatile Indian Stock Market. We propose a technique to analyze and predict the stock price with the help of sentiment analysis and decomposable time series model along with multivariate-linear regression. © 2019 IEEE.Item Dynamic mode-based feature with random mapping for sentiment analysis(Springer Verlag service@springer.de, 2020) Sachin Kumar, S.; Anand Kumar, M.A.; Padannayil, K.P.; Poornachandran, P.Sentiment analysis (SA) or polarity identification is a research topic which receives considerable number of attention. The work in this research attempts to explore the sentiments or opinions in text data related to any event, politics, movies, product reviews, sports, etc. The present article discusses the use of dynamic modes from dynamic mode decomposition (DMD) method with random mapping for sentiment classification. Random mapping is performed using random kitchen sink (RKS) method. The present work aims to explore the use of dynamic modes as the feature for sentiment classification task. In order to conduct the experiment and analysis, the dataset used consists of tweets from SAIL 2015 shared task (tweets in Tamil, Bengali, Hindi) and Malayalam languages. The dataset for Malayalam is prepared by us for the work. The evaluations are performed using accuracy, F1-score, recall, and precision. It is observed from the evaluations that the proposed approach provides competing result. © Springer Nature Singapore Pte Ltd. 2020.Item Analysis of written interactions in open-source communities using RCNN(Institute of Electrical and Electronics Engineers Inc., 2021) Maheshwarkar, A.; Kumar, A.; Gupta, M.Open-source software has proved to be a key pillar in modern-day software development. The growing size of the open-source communities has significantly increased the throughput of these projects. However, larger communities tend to lead to difficulties in communication and openness for newer members. In this paper, we try to analyze the interactions on Github for some of the popular open-source projects. We have created a database of 2500 filtered comments classified into five classes of emotion. We have also proposed a novel RCNN based architecture to detect the sentiment of the comments and perform multiclass text classification. Furthermore, we have discussed possible model integrations with existing open-source platforms and the challenges associated with the implementation. © 2021 IEEE.