Browsing by Author "Pravalika, A."

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A semantic approach to text steganography in sanskrit using numerical encoding
(Springer Verlag service@springer.de, 2019) Keshava, K.; Pravalika, A.; Abhishek, D.V.; Meghana, N.P.; Prasad, G.
Steganography is the art of hiding a message within another so that the presence of the hidden message is indiscernible. People who are not intended to be the recipients of the message should not even suspect that a hidden message exists. Text steganography is challenging as it is difficult to hide data in text without affecting the semantics. Retention of the semantics in the generated stego-text is crucial to minimize suspicion.This paper proposes a technique for text steganography using classical language Sanskrit. As Sanskrit is morphologically rich with a very large vocabulary, it is possible to modify the cover text without affecting the semantics. In addition numerical encoding is used to map a Sanskrit character to a numerical value. This helps in hiding the message effectively. Moreover, in this technique, a key is used for additional security. The key is generated dynamically and is appended to the final message to further add security to the proposed method. The proposed method generated stego-texts with syntactic correctness of 96.7%, semantic correctness of 86.6%, and with a suspicion factor of just 23.4% upon evaluation. © Springer Nature Singapore Pte Ltd. 2019
Domain-specific sentiment analysis approaches for code-mixed social network data
(2017) Pravalika, A.; Oza, V.; Meghana, N.P.; Sowmya, Kamath S.
Sentiment Analysis is one of the prominent research fields in Natural Language Processing because of its widespread real-world applications. Customer preferences, options and experiences can be analyzed through social media, reviews, blogs and other online social networking site data. However, due to increasing informal usage of local languages in social media platforms, multi-lingual or code-mixed data is fast becoming a common occurrence. Mixed code is generated when users use more than a single language in social network comments. Such data presents a significant challenge for applications using sentiment analysis and is yet to be fully explored by researchers. Existing sentiment analysis methods applied to monolingual social data are not suitable for code-mixed data due to the inconsistency in the grammatical structure in these sentences. In this paper, a novel method focused on performing effective sentiment analysis of bilingual sentences written in Hindi and English is proposed, that takes into account linguistic code switching and the grammatical transitions between the two considered languages. Experimental evaluation using real-world, code-mixed datasets obtained from Facebook showed that the proposed approach achieved very good accuracy and was also efficient performance-wise. � 2017 IEEE.
Domain-specific sentiment analysis approaches for code-mixed social network data
(Institute of Electrical and Electronics Engineers Inc., 2017) Pravalika, A.; Oza, V.; Meghana, N.P.; Kamath Sâ€¤, S.
Sentiment Analysis is one of the prominent research fields in Natural Language Processing because of its widespread real-world applications. Customer preferences, options and experiences can be analyzed through social media, reviews, blogs and other online social networking site data. However, due to increasing informal usage of local languages in social media platforms, multi-lingual or code-mixed data is fast becoming a common occurrence. Mixed code is generated when users use more than a single language in social network comments. Such data presents a significant challenge for applications using sentiment analysis and is yet to be fully explored by researchers. Existing sentiment analysis methods applied to monolingual social data are not suitable for code-mixed data due to the inconsistency in the grammatical structure in these sentences. In this paper, a novel method focused on performing effective sentiment analysis of bilingual sentences written in Hindi and English is proposed, that takes into account linguistic code switching and the grammatical transitions between the two considered languages. Experimental evaluation using real-world, code-mixed datasets obtained from Facebook showed that the proposed approach achieved very good accuracy and was also efficient performance-wise. Â© 2017 IEEE.