The key problem to solve in a sentiment analysis of texts is the sentiment polarity classification. Based on the analysis of various factors affecting sentiment classification of texts

it built the sentiment lexicon

extracted affective characteristics

and weighted sentimental features. Then

it used support vector machine (SVM) classifier for emotion recognition and text classification. Finally

it performed the classification model with the corpus data sets on the single platform and the Spark distributed computing platform to analyze its classification accuracy and time cost. The experimental results verify the effectiveness of the text sentimental polarity categorization model on the single platform and on the spark distributed computing platform.