Sentiment Analysis with TextBlob and Python

TextBlob is a python library for Natural Language Processing (NLP). For lexicon-based approaches, a sentiment is defined by its semantic orientation and the intensity of each word in the sentence. ... This requires a pre-defined dictionary classifying negative and positive words.

1. Is TextBlob good for sentiment analysis?
2. How do you do sentiment analysis using TextBlob?
3. How do you use TextBlob in Python?
4. What is difference between NLTK and TextBlob?
5. How do you use spaCy for sentiment analysis?
6. How does sentiment analysis work?
7. How do you do a sentiment analysis in Python?
8. Which is better TextBlob or Vader?
9. How accurate is TextBlob?
10. How do you download TextBlob in Python?
11. What is polarity in Python?
12. What dictionary does TextBlob use?

Is TextBlob good for sentiment analysis?

A big advantage of this is, it is easy to learn and offers a lot of features like sentiment analysis, pos-tagging, noun phrase extraction, etc. It has now become my go-to library for performing NLP tasks. ... If it is your first step in NLP, TextBlob is the perfect library for you to get hands-on with.

How do you do sentiment analysis using TextBlob?

The TextBlob's sentiment property returns a Sentiment object. The polarity indicates sentiment with a value from -1.0 (negative) to 1.0 (positive) with 0.0 being neutral. The subjectivity is a value from 0.0 (objective) to 1.0 (subjective).

How do you use TextBlob in Python?

TextBlob is a Python (2 and 3) library for processing textual data. It provides a simple API for diving into common natural language processing (NLP) tasks such as part-of-speech tagging, noun phrase extraction, sentiment analysis, classification, translation, and more.

What is difference between NLTK and TextBlob?

With regards to the packages you mentioned, as far as I understand Textblob indeed uses a lexicon. NLTK provides a lexicon-based sentiment classification but it also allows you to train your own statistical model. If a knowledge-based or a statistical approach is better for you use-case depends really on your data.

How do you use spaCy for sentiment analysis?

How to Use spaCy for Text Classification

1. Add the textcat component to the existing pipeline.
2. Add valid labels to the textcat component.
3. Load, shuffle, and split your data.
4. Train the model, evaluating on each training loop.
5. Use the trained model to predict the sentiment of non-training data.

How does sentiment analysis work?

Sentiment analysis – otherwise known as opinion mining – is a much bandied about but often misunderstood term. In essence, it is the process of determining the emotional tone behind a series of words, used to gain an understanding of the the attitudes, opinions and emotions expressed within an online mention.

How do you do a sentiment analysis in Python?

1. Step 1 — Installing NLTK and Downloading the Data. ...
2. Step 2 — Tokenizing the Data. ...
3. Step 3 — Normalizing the Data. ...
4. Step 4 — Removing Noise from the Data. ...
5. Step 5 — Determining Word Density. ...
6. Step 6 — Preparing Data for the Model. ...
7. Step 7 — Building and Testing the Model. ...
8. Step 8 — Cleaning Up the Code (Optional)

Which is better TextBlob or Vader?

1 Answer. Vader Sentiment Analysis works better for with texts from social media and in general as well. It is based on lexicons of sentiment-related words. ... I did Twitter sentiment analysis using Vader and was surprised that the sentiments were better compared to textBlob.

How accurate is TextBlob?

In this article, I will discuss the most popular NLP Sentiment analysis packages: Textblob.
...
Comparing results.

How do you download TextBlob in Python?

TextBlob stands on the giant shoulders of NLTK and pattern, and plays nicely with both.

1. Features. Noun phrase extraction. ...
2. Get it now. $ pip install -U textblob $ python -m textblob.download_corpora.
3. Examples. See more examples at the Quickstart guide.
4. Documentation. ...
5. Requirements. ...
6. Project Links. ...
7. License.

What is polarity in Python?

The main focus of this article will be calculating two scores: sentiment polarity and subjectivity using python. ... The range of polarity is from -1 to 1(negative to positive) and will tell us if the text contains positive or negative feedback.

What dictionary does TextBlob use?

In this article, we will explore TextBlob, which is another extremely powerful NLP library for Python. TextBlob is built upon NLTK and provides an easy to use interface to the NLTK library.