Sentiment analysis is a popular research problem to find out within the natural language processing that is dealing with identifying the sentiments or mood of people’s towards elements such as product, text, services and the technology. However, there are few researches conducted on the sentiment analysis of technical article review, so to overcome this deficiency conducts the sentiment analysis over the technical article review and classifying the sentence by overall sentiments that is representing the review is positive or negative. The paper presents the combination of SVM and KNN and find out how much given article sound technically good. The proposed technique is compared with other existing techniques and results shows that the proposed technique is better as compared to the other technique.

Sentiment analysis is similar to opinion mining, which is a popular research problem to search out in the field of NLP. Sentiment analysis determines the perspective of the author and identifies the positive, negative and neutral reviews. It provides the reviews or opinions of people's on text, article and product which can be positive, negative or neutral. Reviews on the different websites, social networking sites is an important source to collect the information regarding various brands of product and new features in technology (e.g. Windows, Mobiles). During the sentiment analysis various classification tools within the NLP are used to find out the positivity and negativity of reviews or comments. The paper presents a length aware hybrid approach to analyses the reviews either as positive or negative and present approach is tested on SuperFetch data set. The present approach is a combination of both supervised machine learning techniques that are Support Vector Machine and K-Nearest Neighbor in which SVM is working great for large size review and KNN is working best for small size review.