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.

sentiment analysis of nepali sentences
sentiment analysis of Nepali sentences

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

For more about machine learning in Nepal click

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2 Thoughts to “Sentiment Analysis of Nepali Sentences (Part-1) |Introduction”

  1. […] Machine Learning supervised machine learning is the process of feed label data to the machine. During machine learning development we need data. […]

  2. […] Sentiment Analysis is the process of opinion mining of user, customer, and reader. Sentiment analysis is part of Machine Learning in which machines able to classify new data according to trained data and model. Be part of Natural Language Processing Sentiment Analysis is widely popular in different fields. Customer reviews analysis in eCommerce, comment analysis in social media and political sentiment analysis in news articles, these are the important field of sentiment analysis. […]

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