Natural Language Processing Approach to Identify Analogous Data in Offline Data Repository

Chandra, Nidhi; Khatri, Sunil Kumar; Som, Subhranil

doi:10.1007/978-981-10-7323-6_6

Cited by 4 publications

(2 citation statements)

References 6 publications

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“…Normally, the sentiment analysis or data analysis in natural language processing is done over big data dataset such as Facebook, twitter, etc. Moreover, sentiment analysis for large volume of data is some more difficult as because of its complexity and part of speech classification [20]. In addition, the sentence which contains positive words may also end with negative sentence.…”

Section: System Model and Problem Definationmentioning

confidence: 99%

Twitter based Data Analysis in Natural Language Processing using a Novel Catboost Recurrent Neural Framework

Narasamma¹,

Sreedevi²

2021

IJACSA

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In recent years, the sentiment analysis using Twitter data is the most prevalent theme in Natural Language Processing (NLP). However, the existing sentiment analysis approaches are having lower performance and accuracy for classification due to the inadequate labeled data and failure to analyze the complex sentences. So, this research develops the novel hybrid machine learning model as Catboost Recurrent Neural Framework (CRNF) with an error pruning mechanism to analyze the Twitter data based on user opinion. Initially, the twitter-based dataset is collected that tweets based on the coronavirus COVID-19 vaccine, which are pre-processed and trained to the system. Furthermore, the proposed CRNF model classifies the sentiments as positive, negative, or neutral. Moreover, the process of sentiment analysis is done through Python and the parameters are calculated. Finally, the attained results in the performance parameters like precision, recall, accuracy and error rate are validated with existing methods.

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Section: System Model and Problem Definationmentioning

confidence: 99%

Twitter based Data Analysis in Natural Language Processing using a Novel Catboost Recurrent Neural Framework

Narasamma¹,

Sreedevi²

2021

IJACSA

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“…Once the solution method in conformity with this issue has been textual content extraction, where in statistics can be categorized primarily based concerning harmony properties. Therefore Nidhi Chandra et al [18] proposed an approach in conformity to detect the comparable text via natural call processing methods. By making use of textual content mining methods, textual content blocks can be condensed to separate the set of documents by means of is evaluated via processing concern of textual content documents.…”

Section: A Novel Hybrid Machine Learning Approach To Classify the Sentiment Value Of Natural Language Processing In Big Datamentioning

confidence: 99%

A Novel Hybrid Machine Learning Approach to Classify the Sentiment Value of Natural Language Processing in Big Data

Suryachandra¹

2020

IJETER

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Natural Language Processing (NLP) is a computer program utilized in big data applications to specify the customer review also it's a major part of AI. Thus NLP strategy is processed with several languages because the language is differing from state to state. Furthermore, sentiment analysis in NLP is advanced in many applications and various languages to evaluate the sentiment value. But part of speech specification for different language is too difficult. To overcome this problem the current research aimed to develop a novel Evolving C4.5 machine learning with Spider Monkey Optimization (EC4.5-ML-SMO) to classify the sentiment analysis in Telugu language efficiently. Moreover, the fitness function of SMO enhanced the accuracy of sentiment classification in Telugu dataset. Finally, the effectiveness of proposed module is evaluated with recent existing works and attained better result by achieving better classification accuracy rate.

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Classification and Recommendation of Mental Health Assistance Events Using an RNN-LSTM, Fast-And-Frugal Trees and Weighted Sum System

Dickson,

Caldwell

2024

Lecture Notes in Computer Science

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Natural Language Processing Approach to Identify Analogous Data in Offline Data Repository

Cited by 4 publications

References 6 publications

Twitter based Data Analysis in Natural Language Processing using a Novel Catboost Recurrent Neural Framework

Twitter based Data Analysis in Natural Language Processing using a Novel Catboost Recurrent Neural Framework

A Novel Hybrid Machine Learning Approach to Classify the Sentiment Value of Natural Language Processing in Big Data

Classification and Recommendation of Mental Health Assistance Events Using an RNN-LSTM, Fast-And-Frugal Trees and Weighted Sum System

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