Sentiment analysis is important part of text mining. From the last few years social networking sites like Facebook, Twitter, and Amazon generates large amount of data, such vast amount of data contains lots of useful information which need to be analyzed. As we all know social networking sites generate data in huge amount not only in terabyte but in petabyte so processing of such large amount of data is big challenge, sentiment analysis is the technique which help to analyzie such raw amount of data and extract useful information from it. The reason behind using Sentiment analysis is that it analyze such large amount of data and extract useful information from it that it analyze such large amount of data and extract useful inform Sentiment analysis helps business and organization because it’s easy for them to know how people feel about their product or services so that they can make better decision or improve their services. For that purpose we have different sentiment analysis techniques like Naïve bayes, Maximum Entropy, SVM which gives correctness of information or provides us accuracy. For sentiment we use machine learning because it train the computer to recognize the emoticon behind the sentence.

Sentiment analysis, Machine Learning. Sentiment Technique

[1]. Kolkur, S., Dantal, G., & Mahe, R. (2015). Study of different levels for sentiment analysis. International Journal of Current Engineering and Technology, 5(2), 768Ű770.
[2]. Wankhede, R., & Thakare, A. N. (2017, April). Design approach for accuracy in movies reviews using sentiment analysis. In Electronics, Communication and Aerospace Technology (ICECA), 2017 International conference of (Vol. 1, pp. 6-11). IEEE.
[3]. .Bhavitha, B. K., Rodrigues, A. P., & Chiplunkar, N. N. (2017, March). Comparative study of machine learning techniques in sentimental analysis. In Inventive Communication and Computational Technologies (ICICCT), 2017 International Conference on (pp. 216-221). IEEE.
[4]. Kumar, K. S., Desai, J., & Majumdar, J. (2016, December). Opinion mining and sentiment analysis on online customer review. In Computational Intelligence and Computing Research (ICCIC), 2016 IEEE International Conference on (pp. 1-4). IEEE.
[5]. Parveen, H., & Pandey, S. (2016, July). Sentiment analysis on Twitter Data-set using Naive Bayes algorithm. In Applied and Theoretical Computing and Communication Technology (iCATccT), 2016 2nd International Conference on (pp. 416-419). IEEE.
[6]. Rana, S., & Singh, A. (2016, October). Comparative analysis of sentiment orientation using SVM and Naive Bayes techniques. In Next Generation Computing Technologies (NGCT), 2016 2nd International Conference on (pp. 106-111). IEEE.
[7]. Prabhat, A., & Khullar, V. Sentiment classification on Big Data using Naïve Bayes and Logistic Regression
[8]. Tripathi, A., & Trivedi, S. K. (2016, October). Sentiment analysis of Indian movie review with various feature selection techniques. In Advances in Computer Applications (ICACA), IEEE International Conference on (pp. 181-185). IEEE.
[9]. Shivaprasad, T. K., & Shetty, J. (2017, March). Sentiment analysis of product reviews: A review. In Inventive Communication and Computational Technologies (ICICCT), 2017 International Conference on (pp. 298-301). IEEE.
[10]. Chachra, A., Mehndiratta, P., & Gupta, M. (2017, August). Sentiment analysis of text using deep convolution neural networks. In Contemporary Computing (IC3), 2017 Tenth International Conference on (pp. 1-6). IEEE.
[11]. Chen, X., Wang, Y., & Liu, Q. (2017). Visual and Textual Sentiment Analysis Using Deep Fusion Convolutional Neural Networks. arXiv preprint arXiv: 1711.07798.
[12]. Alam, M. H., Rahoman, M. M., & Azad, M. A. K. (2017, December). Sentiment analysis for Bangla sentences using convolutional neural network. In Computer and Information Technology (ICCIT), 2017 20th International Conference of (pp. 1-6). IEEE.
[13]. Bhoir, P., & Kolte, S. (2015, December). Sentiment analysis of movie reviews using lexicon approach. In Computational Intelligence and Computing Research (ICCIC), 2015 IEEE International Conference on (pp. 1-6). IEEE.
[14]. Vaitheeswaran, G., & Arockiam, L. (2017). Lexicon Based Approach to Enhance the Accuracy of Sentiment Analysis on Tweets. International Journal Of Computer Science And Information Technology & Security (IJCSITS), 6(3), 33-38.
[15]. Sonawane, S. L., & Kulkarni, P. V. (2017, October). Extracting sentiments from reviews: A lexicon-based approach. In Intelligent Systems and Information Management (ICISIM), 2017 1st International Conference on (pp. 38-43). IEEE
[16]. Zhang, Q., Liu, C., Zhong, S., & Lei, K. (2017, July). Spam comments detection with self-extensible dictionary and text-based features. In Computers and Communications (ISCC), 2017 IEEE Symposium on (pp. 1225-1230). IEEE.
[17]. Manohar, M. Y., & Kulkarni, P. (2017, June). Improvement sarcasm analysis using NLP and corpus based approach. In Intelligent Computing and Control Systems (ICICCS), 2017 International Conference on (pp. 618-622). IEEE.