LDA based topic modeling of journal abstracts

Anupriya, P.; Karpagavalli, S.

doi:10.1109/icaccs.2015.7324058

Cited by 34 publications

(16 citation statements)

References 6 publications

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“…They made use of the K-Nearest Neighbor approach to allocate sentiment labels by constructing a feature vector for each example in the training and test set. Tagging, [8] in current times developed as a common way to sort out vast and vibrant web content. It usually refers to the act of correlating with or allocating some keyword or unit to a piece of data.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

Stress Detection using Natural Language Processing and Machine Learning over social Interactions

Nijhawan

Attigeri

Ananthakrishna

2021

Preprint

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Cyberspace is a vast soapbox for people to post anything that they witness in their day-to-day lives. Subsequently, it can be used as a very effective tool in detecting the stress levels of an individual based on the posts and comments shared by him/her on social networking platforms. We leverage large-scale datasets with tweets to successfully accomplish sentiment analysis with the aid of machine learning algorithms. We take the help of a capable deep learning pre-trained model called BERT to solve the problems which come with sentiment classification. The BERT model outperforms a lot of other well-known models for this job without any sophisticated architecture. We also adopted Latent Dirichlet Allocation which is an unsupervised machine learning method that’s skilled in scanning a group of documents, recognizing the word and phrase patterns within them, and gathering word groups and alike expressions that most precisely illustrate a set of documents. This helps us predict which topic is linked to the textual data. With the aid of the models suggested, we will be able to detect the emotion of users online. We are primarily working with Twitter data because Twitter is a website where people express their thoughts often. In conclusion, this proposal is for the well- being of one’s mental health. The results are evaluated using various metric at macro and micro level and indicate that the trained model detects the status of emotions bases on social interactions.

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Section: Background and Literature Reviewmentioning

confidence: 99%

Stress Detection using Natural Language Processing and Machine Learning over social Interactions

Nijhawan

Attigeri

Ananthakrishna

2021

Preprint

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“…But among all the available topic models, LDA has proven to be a very successful and most popular topic model for organizing a large collection of archives. In LDA, the general assumption is that each document in the corpus is derived from various distinct topics with varying probabilities [18]. The general framework of topic modeling is illustrated in the following figure.…”

Section: Topic Modelling With Latent Dirichlet Allocationmentioning

confidence: 99%

Topic Modelling Extraction of “Mann Ki Baat”

Kandukuri

Haragopal

2021

EJMATH

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The purpose of this study is to give an insight about Textual Data analytics and its application in the analysis of unique public relations campaign”Mann Ki Baat” that was initiated by incumbent Prime Minister of India,honourable “Mr.Narendra Modi” which was initially aired on All India Radio Programme on Vijaya Dashami on October 3rd , 2014 followed by second on November 2nd, 2014 of the same year till December 2019. In this paper, an analytical framework is designed using a powerful technique of textual data analytics “Topic Modelling based on LDA (Latent Dirichlet Allocation)” to accomplish the study. The proposed framework is applied to the corpus of 60 episodes(October 2014 to December 2019) of Mann ki Baat gathered from PMindia website and was analyzed in greater detail. The terms used frequently and recurrence of the topics spoken in his popular monthly radio address program were determined and analyzed from both in statistical and dynamic perspectives.In this context the present study is a first approach of application under the conventional technique “topic modelling” on Mann Ki Baat.Further, this is the principal endeavour to excerpt the themes discussed in radio programme using statistical modelling.

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“…Association rule method like Apriori algorithm was applied as the first step to find a most relative keywords with high support. After that, Latent Dirichlet Allocation [32], [36] (LDA) method was applied as second step to perform topic or subject modeling. After that, fuzzy C-mean algorithm is used as third step to group related keywords and assign central keyword for each subject with a specific membership.…”

Section: B Topic Analyzer Part (Backend Phasementioning

confidence: 99%

Enhanced Search Engine Using Proposed Framework and Ranking Algorithm Based on Semantic Relations

et al. 2019

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Today, most users need search engines to facilitate search and information retrieval processes. Unfortunately, traditional search engines have a significant challenge that they should retrieve high-precision results for a specific unclear query at a minimum response time. Also, a traditional search engine cannot expand a small, ambiguous query based on the meaning of each keyword and their semantic relationship. Therefore, this paper proposes a comprehensive search engine framework that combines the benefits of both a keyword-based and a semantic ontology-based search engine. The main contributions of this work are developing an algorithm for ranking results based on fuzzy membership value and a mathematical model of exploring a semantic relationship between different keywords. In the conducting experiments, eight different test cases were implemented to evaluate the proposed system. Executed test cases have achieved a precision rate of 97% with appropriate response time compared to the relevant systems.

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LDA based topic modeling of journal abstracts

Cited by 34 publications

References 6 publications

Stress Detection using Natural Language Processing and Machine Learning over social Interactions

Stress Detection using Natural Language Processing and Machine Learning over social Interactions

Topic Modelling Extraction of “Mann Ki Baat”

Enhanced Search Engine Using Proposed Framework and Ranking Algorithm Based on Semantic Relations

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