siyizici amashidi carbon fibre abakhiqizi & abaphakeli / i--factory evela e--china. i--wholesale amashidi carbon fibre ngekhwalithi ephakeme njengamanani aphansi / ashibhile, enye ye amashidi carbon fibre brand ehamba phambili evela e--china, hobby carbon cnc technology(shenzhen) limited. okidata mps711c image drum magenta genuine original okidata part no.: 56125802 magenta $186.01	add originalto cart abahumutshi abangamajuda benza amaphutha amalutshwana kakhulu. “ababhali abangamajuda babuyisela okwakubhalwe emibhalweni yesihebheru kunjengoba kwakunjalo.”​—second thoughts on the dead sea scrolls. Le ngxoxo yi-analogue yaseRussia yengxoxo ye-roulette, insizakalo ethandwa kakhulu yokuthandana kwamavidiyo ngokushesha. Akuyona into elula ukubhalisa ngaphambi kokuqala ingxoxo, futhi futhi, uma ungavumeli ukufinyelela kumakrofoni kanye ne-webcam, kuzofanela ushiye leli sayithi. To decide on anything in our day to day life, it is important to have an opinion. Every opinion has a sentiment which helps in carrying decisions easier. There is a huge amount of data on the web which needs to be mined in order to find its sentiment. This paper aims at classifying labelled textual Hindi movie reviews with different classifiers. The dataset has been segregated into positive and negative reviews before processing. The goal of this paper is to predict the sentiment of the online movie review which is in form of documents with varied size. A 10-fold-cross-validation is done in order to check the calibre of the classifier used. The test accuracy is checked using the F1 score considering both precision and recall. A detailed comparison of the unigram and bigram feature‟s accuracy of all the mentioned models is done. The proposed model is classified on the following classifiers Naïve Bayes, Logistic Regression and Random Kitchen Sink algorithm. Each one of these algorithms gave better accuracy when bigram was performed. Out of these four classifying algorithms, it is observed that Naive Bayes Multinomial model has the best accuracy with a 70.37%. Hence, this sentiment analysis model which is a developing big data application is suggested for industrial applications wherein predicting the sentiment is a vital component. More »»