Sentiment Analysis of Nepali Sentences (Part-1) |Introduction
Sentiment Analysis of Nepali Sentences is the process/analysis of sentences and it’s sentiment. Each sentence has either positive or negative or neutral. We can find out the polarity of sentences using a machine learning approach. The large volume of data is used to train our model to predict future data. The computer only recognizes numerical data but we have large categorical data so we need to change such data into numerical form. Calculation steps listed below.
- Collect appropriate data
- Read Data
- Split data and corresponding label
- Make bag of word
- Calculate TF
- Calculate IDF
- Calculate TF*IDF (change categorical data into a vector)
- Use classification model (SVM/Naive Bayes/ANN)
- Split vectors into train and test
- Train data into the model
- Calculate confusion matrix
- Predict new data
All the processes to find the polarity of Nepali sentences discusses an upcoming articles with python code.
Hence, natural language processing is part of machine learning which is relatively hard to analyze. So we will further discuss the analysis of Nepali sentences.
for more detail flow this article
Sentiment Analysis is the Mathematics of Language
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