As TextBlob is a Lexicon-sentiment analyzer. Some predefined rules form the basis. Otherwise, we need to calculate a sentence's polarity.
So only the Lexicon-based sentiment analyzers are also called "Rule-based sentiment analyzers". We use Textblob to analyze complex data and work with textual data. While passing a sentence into Textblob, it gives two outputs. The results are polarity and subjectivity. Polarity is the output that lies between [-1,1]. Where -1 refers to negative sentiment, and +1 refers to positive sentiment. TextBlob is a natural language processing library.
But it sometimes comes with a rule-based sentiment analysis library. The API helps with common NLP tasks like tagging parts of speech and analyzing sentiment. It is a multiclass text classification. The system categorizes the input text into positive, neutral, or negative sentiment.
Textblob sentiment analyzer gives two properties for a given input sentence. Sentiment analysis has applications in various domains. It analyzes user reviews and tweet sentiment. Sentiment analysis is the process of finding and measuring emotions systematically. Sentiment analysis refers to using NLP, text analysis, and biometrics subjectively.
Other types of sentiment analysis.
- Aspect-based sentiment analysis
- grading sentiment analysis (positive, negative, neutral)
- multilingual sentiment analysis
- emotion detection
We can process the sentiment of a sentence in TextBlob. You can do this by looping over the attribute of a TextBlob object. Then, check the attribute of each item in the loop. These methods can't analyze all types of text sentiment because they need to learn more. Polarity is the output that lies between [-1,1]. Here, -1 refers to negative sentiment, and +1 refers to positive sentiment. Users can submit text reviews, comments, or feedback on social networking platforms. Sometimes, also on websites.
TextBlob accesses the averaging technique for computing a sentiment for a single word. We apply this to polarity values to calculate a single word's polarity score. The same rules apply to every single word. The results would be in a combined polarity for larger texts. TextBlob is an easy-to-use open-source Python package. Performing fundamental NLP tasks like tokenization, noun phrase extraction, and classification is easy. You can analyze feelings in text from various places like Twitter, IMDB, or Amazon. Sentiment analysis has various applications, such as analyzing user reviews and tweet sentiment.
The large amounts of shared data as tweets can help extract user sentiments. It provides valuable feedback to improve the quality of products and services alike. The NLTK platform has interfaces to over fifty corpora and lexical sources. We used machine learning techniques and powerful tools to map it. After installing, we can start importing textblob. You can import Python with Spyder, Jupyter Notebook, Pycharm, Sublime, or Command Prompt. Textblob is a versatile tool that you can use for many purposes. It can extract noun phrases, tag parts of speech, and integrate with WordNet. It can also tokenize words, inflect them, and lemmatize them.
Preview of the output that you will get on running this code from your IDE.
In this solution, we used the textblob and nltk libraries.
from textblob import TextBlob
from textblob.sentiments import NaiveBayesAnalyzer
text = "I feel the product is so good"
sent = TextBlob(text)
# The polarity score is a float within the range [-1.0, 1.0]
# where negative value indicates negative text and positive
# value indicates that the given text is positive.
polarity = sent.sentiment.polarity
# The subjectivity is a float within the range [0.0, 1.0] where
# 0.0 is very objective and 1.0 is very subjective.
subjectivity = sent.sentiment.subjectivity
sent = TextBlob(text, analyzer = NaiveBayesAnalyzer())
positive = sent.sentiment.p_pos
negative = sent.sentiment.p_neg
Follow the steps carefully to get the output easily.
- Download and Install the PyCharm Community Edition on your computer.
- Open the terminal and install the required libraries with the following commands.
- Install cv2 - pip install opencv-python.
- Install textblob - pip install textblob.
- Install nltk - pip install nltk.
- Create a new Python file on your IDE.
- Copy the snippet using the 'copy' button and paste it into your python file.
- Run the current file to generate the output.
I hope you found this useful. I have added the link to dependent libraries, and version information in the following sections.
I found this code snippet by searching for ' Get the positive and negative words from a Textblob based on its polarity in Python (Sentimental analysis)' in Kandi. You can try any such use case!
I tested this solution in the following versions. Be mindful of changes when working with other versions.
- The solution is created in PyCharm 2022.3.
- The solution is tested on Python 3.11.1
- textblob version- 0.17.1
- nltk Version - 3.8.1
Using this solution, we are able to use the textblob.sentiment() method with simple steps. This process also facilitates an easy-to-use, hassle-free method to create a hands-on working version of code which would help us to use the textblob.sentiment() method.
Python 8597 Version:0.7.0 License: Permissive (MIT)
1. What is a textblob sentiment, and what is its purpose?
As TextBlob is a Lexicon-based sentiment analyzer. It has some predefined rules. We can calculate a sentence's polarity. So only the Lexicon-based sentiment analyzers are also called "Rule-based sentiment analyzers".
People use Textblob for complex analysis. It works with textual data. When Textblob receives a sentence, it gives two outputs. That is polarity and subjectivity. Polarity is the output that lies between [-1,1]. Here, -1 refers to negative sentiment, and +1 refers to positive sentiment.
2. How does the natural language processing library of TextBlob work for sentiment analysis?
TextBlob processes the polarity and subjectivity of a statement. The range of polarity is [-1,1]. -1 indicating a negative sentiment. 1 indicating a positive sentiment. We use negative words to change the polarity of a sentence.
3. How can someone use TextBlob for text classification and Social Media Sentiment Analysis?
Now, developers have created a wide and significant type of social media platform. It has become an important part of modern life. You can share your thoughts on social media or online stores by leaving reviews or comments.
Movie reviews: Analysing online movie reviews. To get information from the audience about the movie
Online food reviews: Analyzing user comments. To establish feedback on how people feel about food.
Movie reviews: Analyzing online movie reviews to gather audience feedback on the film.
Online food reviews: Analyzing user comments to reflect how people feel about food.
4. How do you calculate sentiment polarity with TextBlob?
Textblob gives two outputs while a sentence is passing. That is polarity and subjectivity. Polarity is the output that lies between [-1,1]. Here, -1 refers to negative sentiment. +1 refers to positive sentiment. Subjectivity is the output. It is lies within [0,1]. It refers to personal opinions and judgments.
5. Are there any advantages or disadvantages to using text mining?
Machine learning and information retrieval generally adopt this method for text mining. The method has both advantages and disadvantages. The advantage is that it gives the most efficient computation of terms. The disadvantage is that one term has multiple meanings. Otherwise, multiple terms have the same meaning.