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Data Science Project For Beginners-Sentimental Analysis
With the rising prominence for Data Science, the job-role of a Data Scientist is being considered as the hottest job in-demand. With the growing prominence for Big Data, Data Scientists have become the need of the hour for the enterprises across various sectors. So, enterprises have started recruiting Data Scientists at large. To get hired as a Data Scientist, you need to set your complete focus on developing skills in the latest tools, techniques & technologies that are used in this domain.
Also, as there’s a huge completion for the Data Science job-roles, to stand out from the crowd you need to build a strong profile that showcases all your skills & the best way to do this is to get started with the process of building your project portfolio. Become an expert in Data Science & build your own project portfolio by working on multiple projects in real-time being a part of our Data Science Training In Hyderabad program.
Here below is the list of some of the most innovative projects for beginners Data Science..
Data Science Project Idea-Sentiment Analysis
The process of analyzing the words that make up the sentence to determine sentiments that are associated with it is known as Sentiment Analysis. This form of analysis is very crucial for the enterprises to understand their customer opinions from by analyzing the textual data which is collected from website & social media reviews. This is a type of classification where the classes may be binary (positive and negative) or multiple (happy, angry, sad, disgusted,..).
This is a challenging project idea that requires extensive knowledge of packages & libraries in either Python or R. You will be required to work on any of the classification algorithms like Linear Regression, Naive Bayes, Support Vector Machines, RNN derivatives, LSTM and GRU. As a part of this project, you will also be working on Natural Language Processing.
Sentiment Analysis is an essential topic in machine learning. It has numerous applications in multiple fields.