The impact of preprocessing steps on the accuracy of machine learning algorithms in sentiment analysis

Probabilistic Neural Network and Word Embedding for Sentiment Analysis

“…Previous study [5] used some ML algorithms to enhance the accuracy of sentiment analysis, and in this study we will use some DL techniques for further incurring the accuracy.…”

“…Previous work [5] suggested the impact of preprocessing technique on the accuracy of sentiment analysis of different ML algorithms. In that work it was suggested that removing of emoticons and stopwords alongwith stemming and word vectorization can improve the efficiency and accuracy of ML algorithms which are Naive Bayes, Support Vector Machine and Maximum Entropy.…”

“…For this work, we used the previous dataset of our research work which contains the Twittrat keyword list. This list includes 174 positive and 185 negative words [5]. For each tweet, we will count the number of negative and positive keywords that appears.…”

“…After the training procedure as shown in (4) and (5), the input classification pattern can be commenced as under:…”

“…Previously for sentiment analysis, some DL techniques were used which were enormously effective, such as word2vector [4]. In our this work we will use WE [4] with Probabilistic Neural Network (PNN) to increase the accuracy of sentiment analysis [5]. PNN is extensively adopted by researchers for pattern recognition and classification.…”