Papers by VENKATESWARLU NAIK
Social Network Analysis and Mining, 2021
Nowadays, the big data is ruling the entire digital world with its applications and facilities. T... more Nowadays, the big data is ruling the entire digital world with its applications and facilities. Thus to run the online services in better way some of the machine learning model is utilized, also the machine learning strategy is became a trending field in big data; hence the success of online services or business is based upon the customer reviews. Almost the review contains neutral, positive, and negative sentiment value; this specification is done using natural Language Processing (NLP). Manual classification of sentiment value is a difficult task so that the Natural Language Processing (NLP) scheme is used which is processed using a machine learning strategy. Moreover, the part of Speech Specification for different language is difficult. To overcome this issue, the current research developed a CatBoost machine learning model with Less Error Pruning (LEP)-Shortest Description Length (SDL) and Ant Lion Optimization (MOALO) approach to classify the sentiment values in Telugu reviews. The purpose of using LEP-SDL is to remove unwanted characters and make the classification process easier. Several error removing models are available for machine learning process but those models are ineffective when it comes under to remove the error in Telugu Language, so that LEP-SDL model is developed here. Moreover, the fitness function of ALO is used in the catboost classification module improves the accuracy of sentiment classification. In addition, the proposed approach is implemented using python; the efficiency of the proposed model is compared with recent existing works and achieved better results by attaining high accuracy and precision rate of sentiment classification. The obtained results were justified that the proposed model is applicable for online services or businesses to classify the sentiment rates of each customer.
International Journal of Sensors, Wireless Communications and Control, 2020
Aims: The proposed research work is on an evolutionary enhanced method for sentiment or emotion c... more Aims: The proposed research work is on an evolutionary enhanced method for sentiment or emotion classification on unstructured review text in the big data field. The sentiment analysis plays a vital role for current generation of people for extracting valid decision points about any aspect such as movie ratings, education institute or politics ratings, etc. The proposed hybrid approach combined the optimal feature selection using Particle Swarm Optimization (PSO) and sentiment classification through Support Vector Machine (SVM). The current approach performance is evaluated with statistical measures, such as precision, recall, sensitivity, specificity, and was compared with the existing approaches. The earlier authors have achieved an accuracy of sentiment classifier in the English text up to 94% as of now. In the proposed scheme, an average accuracy of sentiment classifier on distinguishing datasets outperformed as 99% by tuning various parameters of SVM, such as constant c value a...
An Enhanced Unsupervised Learning Approach for Sentiment Analysis Using Extraction of Tri-Co-Occurrence Words Phrases
Proceedings of the Second International Conference on Computational Intelligence and Informatics, 2018
This article reveals an unsupervised learning approach for determining the polarity of unstructur... more This article reveals an unsupervised learning approach for determining the polarity of unstructured text in big data environment. The key inspiration for sentiment analysis research is essential for end users or e-commerce firms with local and global languages who expressed views about certain entities or subjects in social media or blogs or web resources. In proposed approach, applied an unsupervised learning approach with the help of idiom pattern extraction in determining favorable or unfavorable opinions or sentiments. Prior methods have achieved precision of sentiment classification accuracy on English language text up to 81.33% on a movie dataset with two co-occurrences of sentiment words phrases. This approach addressed the enhancement of sentiment classification accuracy in unstructured text in a big data environment with the help of extracting phrase patterns with tri-co-occurrences sentiment words. Proposed approach used two datasets such as cornel movie review and university selection datasets that are publicly available. Lastly, a review document is classified after comprehensive computation of semantic orientation of the phrases into positive or negative.
An Enhanced Unsupervised Learning Approach for Sentiment Analysis Using Extraction of Tri-Co-Occurrence Words Phrases
This article reveals an unsupervised learning approach for determining the polarity of unstructur... more This article reveals an unsupervised learning approach for determining the polarity of unstructured text in big data environment. The key inspiration for sentiment analysis research is essential for end users or e-commerce firms with local and global languages who expressed views about certain entities or subjects in social media or blogs or web resources. In proposed approach, applied an unsupervised learning approach with the help of idiom pattern extraction in determining favorable or unfavorable opinions or sentiments. Prior methods have achieved precision of sentiment classification accuracy on English language text up to 81.33% on a movie dataset with two co-occurrences of sentiment words phrases. This approach addressed the enhancement of sentiment classification accuracy in unstructured text in a big data environment with the help of extracting phrase patterns with tri-co-occurrences sentiment words. Proposed approach used two datasets such as cornel movie review and univers...
An expert system approach for legal reasoning in acquire immovable property
2014 First International Conference on Networks & Soft Computing (ICNSC2014), 2014
ABSTRACT In modern system, legal rules, knowledge and dynamic laws are numerous. Legal reasoning ... more ABSTRACT In modern system, legal rules, knowledge and dynamic laws are numerous. Legal reasoning is more multifaceted in distinguishing fields to make fair and accurate decisions. To normalize legal reasoning route appropriately it requires analysis and expertness. Similarly, to automate the complex legal reasoning, it is required an expert system. In this paper, we propose an expert system model in the area of acquisition of immovable property such as tangible property (land or house etc...). The contexts to be performed in legal reasoning in this domain are (i) to acquire immovable property (ii) to transfer immovable property (iii) to mortgage immovable property (iv)to gifting immovable property. We propose empirical approach with the help of integrating rule based technique along with case based reasoning and building hybrid system by involving interpreting constitutions, statutes, and regulations in balancing fundamental principles to make ultimate decisions. Our model is an interactive that it allows the end users to answer with respect to questions asked by the interactive legal expert system as a result system which is helpful to legal decision advisory system or system for classification of facts or fact finding diagnostic system.
Uploads
Papers by VENKATESWARLU NAIK