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RISS 인기검색어
- 검색키워드
- 저자 : Tanwar Rohit (검색결과 2 건)
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Machine learning based aspect level sentiment analysis for Amazon products
Nandal Neha,Tanwar Rohit,Pruthi Jyoti 대한공간정보학회 2020 Spatial Information Research Vol.28 No.5
The field of sentiment analysis is widely utilized for analyzing the text data and then extracting the sentiment component out of that. The online commercial websites generates a huge amount of textual data via customer’s reviews, comments, feedbacks and tweets every day. Aspect level analysis of this data provides a great help to retailers in better understanding of customer’s expectations and then shaping their policies accordingly. However, a number of algorithms are existing these days to do aspect level sentiment detection on specified domains, but a few consider bipolar words (words which changes polarity according to context) while doing analyses. In this paper, a novel approach has been presented that utilize aspect level sentiment detection, which focuses on the features of the item. The work has been implemented and tested on Amazon customer reviews (crawled data) where aspect terms are identified first for each review. The system performs pre-processing operations like stemming, tokenization, casing, stop-word removal on the dataset to extract meaningful information and finally gives a rank for its classification in negativity or positivity.
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A roadmap of steganography tools: conventional to modern
Pilania Urmila,Tanwar Rohit,Gupta Prinima,Choudhury Tanupriya 대한공간정보학회 2021 Spatial Information Research Vol.29 No.5
Steganography emerged as an effective technology for securing the data over the network. Its specificity of concealing the existence of the secret data supports its application in securing the information in the modern era. The desire of industry and support from various governments motivated the researchers to develop steganography tools. These spatial and transform domain tools implement different steganography techniques either solo or as a hybrid using a wide range of media as a cover file for hiding various types of data. In this paper, a systematic study of the steganography tools developed in the last three decades has been done. The comparative analysis of these tools based on specified parameters represents their strengths, limitations, applicability, and scope for future work as well. OpenPuff steganography tool spawns a huge acceptance by academics and professionals. This paper also analyze the performance of the OpenPuff tool on some unexplored parameters to validate and justify its performance.
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