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Understanding Python Sentimental Analysis Methodology

This free online course allows you to understand Python sentimental analysis methodology and heightens your expertise.

Publisher: Kelvin Fosu
Does the idea of automation excite you? This free online course gives you a sound understanding of Python sentimental analysis methodology. To take this course, a good command of Python will be beneficial. We will introduce you to TextBlob, along with lemmatization and tokenization methods. Then, we will delve into the automation domain, and you’ll become a master-king of sentimental classification and visualization.
Understanding Python Sentimental Analysis Methodology
  • Duration

    1.5-3 Hours
  • Students

    12
  • Accreditation

    CPD

Description

Modules

Outcome

Certification

View course modules

Description

Did it ever occur to you that you can analyze a piece of text to discover the hidden sentiment behind it? As astonishing as it may appear, this is relatively possible through Python sentimental analysis, a specific methodology to analyse a piece of text. You will discover that this process calls for a combination of machine learning and language processing, with the backdrop of a specific programming language. This programming language is where Python comes into play. Sentimental analysis has become an important topic, particularly in the business field, owing to its relevance in solving business problems. This course is about understanding Python Sentimental Analysis Methodology. Sentimental analysis, more often referred to as sentiment classification, is about text classification tasks. If you are supplied with a phrase, a classifier is theorized to highlight the sentiment behind it. Sentimental analysis decides if the sentiment is positive, negative, or even neutral.

As you continue, you will better understand sentimental analysis and the importance of Python in the process. Since computers cannot transform abstract language, we need to resort to a programming language to convert the text under analysis to unique numbers, interpreted to highlight the sentiment. Python is the most widely used programming language because it lets you work while integrating systems more effectively. In this course, we will emphasise PyCharm. PyCharm can provide all the tools you need for better productive Python development. Unlike Visual Studio, Pycharm is an excellent IDE for Python, an Integrated Development Environment. Providing more reliable code completion and the best code inspections, Pycharm is ideal for quick fixes, leading to better performance in sentimental analysis.

Another essential notion you will learn in this course relates to TextBlob. It is a specific Python library to process textual data. TextBlob provides a simple API allowing you to dive into Natural Language Processing (NLP), including part of speech tagging, noun phrase extraction and sentiment analysis. Other NLP includes translation and classification, to list just a few. In this course, we will introduce you to polarity and subjectivity, which are TextBlob-related concepts. TextBlob concepts lie within the range of negative one and positive one; polarity is just a float, with negative one meaning negative statement and positive one meaning a positive statement. Subjectivity, a float-related notion, lies within the range of zero and one. A personal opinion, a judgement, and even an emotion are generally referred to as subjective sentences. Conversely, only factual information can be objective. This course will undoubtedly be helpful to professionals and students alike who have an excellent command of Python and are interested in data science.

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