The Experts below are selected from a list of 159 Experts worldwide ranked by ideXlab platform
Shaveta Bhatia - One of the best experts on this subject based on the ideXlab platform.
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course recommender system architecture with Sentiment Score
Social Science Research Network, 2021Co-Authors: Raj Kumar, Shaveta BhatiaAbstract:Recommender systems are widely used these days for recommendation of courses. The recommender systems have gained acceptance among users because of their better results. This paper proposes a novel model on optimizing recommender system using Sentiment Score as a booster method. TOPSYS method is best suited for multi-criteria decision making (MCDM) problems. Sentiment Analysis has been added to existing recommender systems in the form of Sentiment Score for improving the recommendations. The Sentiment analysis will add novelty and will eventually improve recommender system.
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an efficient content based e learning recommender system using k l divergence and Sentiment Score
Design Engineering, 2021Co-Authors: Raj Kumar, Shaveta BhatiaAbstract:E-learning is of paramount importance these days. The enormous growth of e-learning content has resulted into the rise of recommender systems.E-learning recommender systems are required for recommending the best recommendations according to the need of an individual.Raj Kumar and Bhatia have calculated cosine similarity for generating recommendations for a particular student [1]. The cosine similarity is used to know the directional similarity between the courses. However, keeping in mind the probabilistic nature of data under consideration. It will be better to use K-L divergence for more accurate recommendations. In this paper, ordinal data has been used and K-L divergence is applied to achieve more accurate recommendations. Further, the Sentiment Score associated with course is applied to improve efficiency of the course recommendations.
Raj Kumar - One of the best experts on this subject based on the ideXlab platform.
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course recommender system architecture with Sentiment Score
Social Science Research Network, 2021Co-Authors: Raj Kumar, Shaveta BhatiaAbstract:Recommender systems are widely used these days for recommendation of courses. The recommender systems have gained acceptance among users because of their better results. This paper proposes a novel model on optimizing recommender system using Sentiment Score as a booster method. TOPSYS method is best suited for multi-criteria decision making (MCDM) problems. Sentiment Analysis has been added to existing recommender systems in the form of Sentiment Score for improving the recommendations. The Sentiment analysis will add novelty and will eventually improve recommender system.
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an efficient content based e learning recommender system using k l divergence and Sentiment Score
Design Engineering, 2021Co-Authors: Raj Kumar, Shaveta BhatiaAbstract:E-learning is of paramount importance these days. The enormous growth of e-learning content has resulted into the rise of recommender systems.E-learning recommender systems are required for recommending the best recommendations according to the need of an individual.Raj Kumar and Bhatia have calculated cosine similarity for generating recommendations for a particular student [1]. The cosine similarity is used to know the directional similarity between the courses. However, keeping in mind the probabilistic nature of data under consideration. It will be better to use K-L divergence for more accurate recommendations. In this paper, ordinal data has been used and K-L divergence is applied to achieve more accurate recommendations. Further, the Sentiment Score associated with course is applied to improve efficiency of the course recommendations.
Imran Sarwar Bajwa - One of the best experts on this subject based on the ideXlab platform.
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design and application of a multi variant expert system using apache hadoop framework
Sustainability, 2018Co-Authors: Muhammad Ibrahim, Imran Sarwar BajwaAbstract:Movie recommender expert systems are valuable tools to provide recommendation services to users. However, the existing movie recommenders are technically lacking in two areas: first, the available movie recommender systems give general recommendations; secondly, existing recommender systems use either quantitative (likes, ratings, etc.) or qualitative data (polarity Score, Sentiment Score, etc.) for achieving the movie recommendations. A novel approach is presented in this paper that not only provides topic-based (fiction, comedy, horror, etc.) movie recommendation but also uses both quantitative and qualitative data to achieve a true and relevant recommendation of a movie relevant to a topic. The used approach relies on SentiwordNet and tf-idf similarity measures to calculate the polarity Score from user reviews, which represent the qualitative aspect of likeness of a movie. Similarly, three quantitative variables (such as likes, ratings, and votes) are used to get final a recommendation Score. A fuzzy logic module decides the recommendation category based on this final recommendation Score. The proposed approach uses a big data technology, “Hadoop” to handle data diversity and heterogeneity in an efficient manner. An Android application collaborates with a web-bot to use recommendation services and show topic-based recommendation to users.
Nick Bassiliades - One of the best experts on this subject based on the ideXlab platform.
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ontology based Sentiment analysis of twitter posts
Expert Systems With Applications, 2013Co-Authors: Efstratios Kontopoulos, Christos Berberidis, Theologos Dergiades, Nick BassiliadesAbstract:The emergence of Web 2.0 has drastically altered the way users perceive the Internet, by improving information sharing, collaboration and interoperability. Micro-blogging is one of the most popular Web 2.0 applications and related services, like Twitter, have evolved into a practical means for sharing opinions on almost all aspects of everyday life. Consequently, micro-blogging web sites have since become rich data sources for opinion mining and Sentiment analysis. Towards this direction, text-based Sentiment classifiers often prove inefficient, since tweets typically do not consist of representative and syntactically consistent words, due to the imposed character limit. This paper proposes the deployment of original ontology-based techniques towards a more efficient Sentiment analysis of Twitter posts. The novelty of the proposed approach is that posts are not simply characterized by a Sentiment Score, as is the case with machine learning-based classifiers, but instead receive a Sentiment grade for each distinct notion in the post. Overall, our proposed architecture results in a more detailed analysis of post opinions regarding a specific topic.
Olga Vechtomova - One of the best experts on this subject based on the ideXlab platform.
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uw finsent at semeval 2017 task 5 Sentiment analysis on financial news headlines using training dataset augmentation
Meeting of the Association for Computational Linguistics, 2017Co-Authors: Vineet John, Olga VechtomovaAbstract:This paper discusses the approach taken by the UWaterloo team to arrive at a solution for the Fine-Grained Sentiment Analysis problem posed by Task 5 of SemEval 2017. The paper describes the document vectorization and Sentiment Score prediction techniques used, as well as the design and implementation decisions taken while building the system for this task. The system uses text vectorization models, such as N-gram, TF-IDF and paragraph embeddings, coupled with regression model variants to predict the Sentiment Scores. Amongst the methods examined, unigrams and bigrams coupled with simple linear regression obtained the best baseline accuracy. The paper also explores data augmentation methods to supplement the training dataset. This system was designed for Subtask 2 (News Statements and Headlines).
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Sentiment analysis on financial news headlines using training dataset augmentation
arXiv: Computation and Language, 2017Co-Authors: Vineet John, Olga VechtomovaAbstract:This paper discusses the approach taken by the UWaterloo team to arrive at a solution for the Fine-Grained Sentiment Analysis problem posed by Task 5 of SemEval 2017. The paper describes the document vectorization and Sentiment Score prediction techniques used, as well as the design and implementation decisions taken while building the system for this task. The system uses text vectorization models, such as N-gram, TF-IDF and paragraph embeddings, coupled with regression model variants to predict the Sentiment Scores. Amongst the methods examined, unigrams and bigrams coupled with simple linear regression obtained the best baseline accuracy. The paper also explores data augmentation methods to supplement the training dataset. This system was designed for Subtask 2 (News Statements and Headlines).
