Dr. Adarsh M J

@aitckm.edu.in

Associate Professor and Head , Computer Science and Engineering ( Data Science)
Adichunchanagiri Institute of Technology

Dr. Adarsh M J


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https://researchid.co/adarshmj

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Science, Artificial Intelligence, Decision Sciences, Management Science and Operations Research
4

Scopus Publications

75

Scholar Citations

4

Scholar h-index

3

Scholar i10-index

Scopus Publications

  • Effectiveness and Influence of Parts of Speech like adjectives and interjections for examining the feelings in sentences containing sarcasm: A study
    Adarsh M J, Pushpa Ravikumar
    2nd IEEE International Conference on Advances in Information Technology Icait 2024 Proceedings, 2024
    In contemporary text analytics, examining the feelings in customer evaluations has taken centre stage. It is difficult to identify emotions in sentences written with irony. Social media portals and e-commerce portals are offering users to come up with comments and reviews with realistic funny emotions. As a way to identify the feelings expressed in sentences written in a mocking tone purpose, we have attempted to highlight the Adjectives function in this study by taking into account positive and negative adjectives as well as an interjection, both good and bad words. We propose the Adjective-based Sarcasm Detection Algorithm (ABSA) and the Interjection-based Sarcasm Detection Algorithm (IBSA) based on lexicon approach as machine learning approaches won't yield better results in detection of irony and have compared it with the existing approach with more sarcastic sentences. Our F score results for ABSA and IBSA are 0.86 and 0.91, respectively. This paper sheds light on investigating humour or irony in context of parts of speech (POS) words.
  • Sarcasm detection in Text Data to bring out genuine sentiments for Sentimental Analysis
    Adarsh M J, Pushpa Ravikumar
    1st IEEE International Conference on Advances in Information Technology Icait 2019 Proceedings, 2019
    The growth of people using social media and E commerce in the modern world has influenced the people in the way they think, they communicate and act. Sentiments are expressed in the posts written by the users, views expressed by the customers and etc. Detection of sentiments in the posts on social media platforms and e commerce portals are helping to find new avenues for business Ventures. Most of the time users and customers write a comment or express a view which in depth will be opposite of what they mean to say by bringing in Irony or sarcasm in the statements. Detection of Sarcasm or Irony in sentences has become a challenging task. In this paper an attempt is made to bring out the negativity in positive sentences and positivity in negative sentences by calculating polarity scores using Sentiwordnet. Identification of sarcasm in sentences will help in bringing out genuine sentiments.
  • An Effective Method of Predicting the Polarity of Airline Tweets using sentimental Analysis
    M J Adarsh, Pushpa Ravikumar
    Proceedings of the 4th International Conference on Electrical Energy Systems Icees 2018, 2018
    Sentiment Analysis is an approach of analyzing the sentiments using text analysis and Natural Language processing Methods. In Sentiment Analysis, the conceptive information is identified and extracted from the various sources. It aims to identify the mindset of a user across various aspects. Globally, it is used for Opinion extraction and recognition of sentiments, which helps Business establishments in understanding the needs of the end users. In this Paper, an effective yet simple approach of sentiment analysis is presented, which involves calculation of scores based on positive and Negative words. Tweets are classified positive, Negative and Neutral based on the scores.
  • A comparative method for different aspect based products features in online reviews of different languages
    A. M Manushree, M. J Adarsh, Pushpa Ravi Kumar
    Rteict 2017 2nd IEEE International Conference on Recent Trends in Electronics Information and Communication Technology Proceedings, 2017
    User review is the most valuable data. This review contains data in the form of opinion about a particular product or entity in detail. Using reviews, conclusion about any product can be drawn and also, user related complaints while using the product or expectation of users from the product manufacturer can be identified clearly. These reviews may also serve as a feedback to the product manufacturer to improve or to correct their flaws regarding that product. The challenge is to manage and analyze the huge sets of these reviews in a convenient way. While analyzing and managing reviews there exists a set of problems such as words, which are misspelled, grammatically incorrect sentences and also reviews written in languages other than English. The analyzer cannot expect the user to write reviews in a way as he/she expects. The current trendy generation believes the use of short forms, misspelled words as a current trend. Therefore, it is important to deal with these trendy problems as well. This paper proposes a method that put forwards an idea to deal with problem related to misspelled words in a product review and also an attempt to deal with multiple languages at a time and a comparison between SentiWordNet and TextBlob has been made to show the difference in accuracy while computation. The results are obtained based on the polarity score. The polarity score for each sentence in the review is assigned using the TextBlob. This method also put forwards a technique, which can overcome most commonly committed spell mistakes by the reviewer in context of needed aspects, also a solution to deal with reviews written in other languages.

RECENT SCHOLAR PUBLICATIONS

  • Machine Learning-Based Predictions of Traffic Accidents Severeness
    VH Kavana, MJ Adarsh
    JNNCE Journal of Engineering and Management, special issue SP02, 118-123 , 2025
    2025.0
    Citations: 1
  • Effectiveness and Influence of Parts of Speech like adjectives and interjections for examining the feelings in sentences containing sarcasm: A study
    MJ Adarsh, P Ravikumar
    2024 Second International Conference on Advances in Information Technology … , 2024
    2024.0
    Citations: 2
  • Optimizing Sentiment Analysis through Text Compression: Current Trends and Future Directions
    MJ Adarsh, S Acharya
    2024.0
  • Facial Expression Based Sentimental Analysis Using CNN
    MS Adiga, MJ Adarsh
    Int. J. Innov. Technol. Explor. Eng 13, 45-49 , 2024
    2024.0
    Citations: 1
  • A Novel Social Media Data Analytics Framework for Efficient Decision-making in Business
    MJ Adarsh, V Kulshrestha, K Kavitha, KR Jagdale
    Emerging Trends in Smart Societies, 65-68 , 2024
    2024.0
  • Sarcasm detection in text data to bring out genuine sentiments for sentimental analysis
    MJ Adarsh, P Ravikumar
    2019 1st international conference on advances in information technology … , 2019
    2019.0
    Citations: 14
  • Sentiment Analysis of Customer Feedback on Restaurant Reviews
    DP Ravikumar, MA MJ
    2019.0
  • An Effective approach for sarcasm detection in Text data for Sentimental Analysis
    DPR Adarsh M J
    International Journal of Engineering and Technology(IJET) 7 (39), 136-138 , 2018
    2018.0
    Citations: 3
  • An effective method of predicting the polarity of airline tweets using sentimental analysis
    MJ Adarsh, P Ravikumar
    2018 4th international conference on electrical energy systems (ICEES), 676-679 , 2018
    2018.0
    Citations: 25
  • Sentiment Analysis of Customer Feedback on Restaurants
    C Spoorthi, PR Kumar, MJ Adarsh
    INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT) 6 (ICRTT … , 2018
    2018.0
    Citations: 4
  • A comparative method for different aspect based products features in online reviews of different languages
    AM Manushree, MJ Adarsh, PR Kumar
    2017 2nd IEEE international conference on recent trends in electronics … , 2017
    2017.0
    Citations: 4
  • A Method to Overcome Misspelled Words in Reviews using Pattren Matching Techniquue
    MAM Adarsh M J
    International Journal of Engineering Research & Technology (IJERT) 5 (6 … , 2017
    2017.0
  • Survey: Twitter data analysis using opinion mining
    DPR Adarsh M J
    International Journal of Computer Applications (IJCA) 128 (5), 34-36 , 2015
    2015.0
    Citations: 21
  • Survey: Client Data Cache Invalidation Mechanism in Trust based Wireless Mobile Networks
    AT Adarsh M J
    International Journal of Computer Applications (IJCA) 2, 9-11 , 2014
    2014.0
  • Polypathology Prediction Using Machine Learning
    S Kadam, MJ Adarsh
  • IoT-driven Parkinson’s disease identification
    SR Keerthi, MJ Adarsh
  • Deep Learning-Based Car Detection Techniques
    HS Lavanya, MJ Adarsh
  • Student Mental Health Assessment via Machine Learning Techniques
    RK Srinidhi, MJ Adarsh
  • DETECTION OF SPAM SMS
    D Sneha, MJ Adarsh
  • Flood Forecasting Using Machine Learning
    HP Spoorthy, MJ Adarsh

MOST CITED SCHOLAR PUBLICATIONS

  • An effective method of predicting the polarity of airline tweets using sentimental analysis
    MJ Adarsh, P Ravikumar
    2018 4th international conference on electrical energy systems (ICEES), 676-679 , 2018
    2018.0
    Citations: 25
  • Survey: Twitter data analysis using opinion mining
    DPR Adarsh M J
    International Journal of Computer Applications (IJCA) 128 (5), 34-36 , 2015
    2015.0
    Citations: 21
  • Sarcasm detection in text data to bring out genuine sentiments for sentimental analysis
    MJ Adarsh, P Ravikumar
    2019 1st international conference on advances in information technology … , 2019
    2019.0
    Citations: 14
  • Sentiment Analysis of Customer Feedback on Restaurants
    C Spoorthi, PR Kumar, MJ Adarsh
    INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT) 6 (ICRTT … , 2018
    2018.0
    Citations: 4
  • A comparative method for different aspect based products features in online reviews of different languages
    AM Manushree, MJ Adarsh, PR Kumar
    2017 2nd IEEE international conference on recent trends in electronics … , 2017
    2017.0
    Citations: 4
  • An Effective approach for sarcasm detection in Text data for Sentimental Analysis
    DPR Adarsh M J
    International Journal of Engineering and Technology(IJET) 7 (39), 136-138 , 2018
    2018.0
    Citations: 3
  • Effectiveness and Influence of Parts of Speech like adjectives and interjections for examining the feelings in sentences containing sarcasm: A study
    MJ Adarsh, P Ravikumar
    2024 Second International Conference on Advances in Information Technology … , 2024
    2024.0
    Citations: 2
  • Machine Learning-Based Predictions of Traffic Accidents Severeness
    VH Kavana, MJ Adarsh
    JNNCE Journal of Engineering and Management, special issue SP02, 118-123 , 2025
    2025.0
    Citations: 1
  • Facial Expression Based Sentimental Analysis Using CNN
    MS Adiga, MJ Adarsh
    Int. J. Innov. Technol. Explor. Eng 13, 45-49 , 2024
    2024.0
    Citations: 1
  • Optimizing Sentiment Analysis through Text Compression: Current Trends and Future Directions
    MJ Adarsh, S Acharya
    2024.0
  • A Novel Social Media Data Analytics Framework for Efficient Decision-making in Business
    MJ Adarsh, V Kulshrestha, K Kavitha, KR Jagdale
    Emerging Trends in Smart Societies, 65-68 , 2024
    2024.0
  • Sentiment Analysis of Customer Feedback on Restaurant Reviews
    DP Ravikumar, MA MJ
    2019.0
  • A Method to Overcome Misspelled Words in Reviews using Pattren Matching Techniquue
    MAM Adarsh M J
    International Journal of Engineering Research & Technology (IJERT) 5 (6 … , 2017
    2017.0
  • Survey: Client Data Cache Invalidation Mechanism in Trust based Wireless Mobile Networks
    AT Adarsh M J
    International Journal of Computer Applications (IJCA) 2, 9-11 , 2014
    2014.0
  • Polypathology Prediction Using Machine Learning
    S Kadam, MJ Adarsh
  • IoT-driven Parkinson’s disease identification
    SR Keerthi, MJ Adarsh
  • Deep Learning-Based Car Detection Techniques
    HS Lavanya, MJ Adarsh
  • Student Mental Health Assessment via Machine Learning Techniques
    RK Srinidhi, MJ Adarsh
  • DETECTION OF SPAM SMS
    D Sneha, MJ Adarsh
  • Flood Forecasting Using Machine Learning
    HP Spoorthy, MJ Adarsh