Getting Started With Sentimental Analysis

Introduction
Sentiment analysis involves using computers to analyze people's emotions, opinions, attitudes, and sentiments. It is a significant issue that is becoming increasingly important in both business and society. While sentiment analysis poses various research challenges, it can provide valuable insights to anyone interested in analyzing opinions and social media. Despite its widespread use, it lacks a clear definition of the task due to its many overlapping concepts and sub-tasks. As a vital area of scientific research, it is necessary to eliminate this ambiguity and define various directions and aspects of sentiment analysis in detail. This is especially important for students, scholars, and developers new to the field. Sentiment analysis involves several natural language processing tasks that have different objectives, including sentiment classification, opinion information extraction, opinion summarization, and sentiment retrieval, and each task has multiple solution paths.
In this article, we will explore the fundamentals of sentiment analysis, including the different types of sentiment analysis tasks, the most popular techniques and tools for sentiment analysis, and some practical examples and code snippets in Python that demonstrate how to perform sentiment analysis on your own text data.

Types of Sentiment Analysis Tasks

Sentiment analysis involves several types of natural language processing tasks, each with its own objectives and challenges. Some of the most common types of sentiment analysis tasks include:

Sentiment classification: This involves classifying text into positive, negative, or neutral categories based on the expressed sentiment.

Aspect-based sentiment analysis: This involves identifying the sentiment associated with different aspects of a particular entity or product, such as its features or attributes.

Opinion mining: This involves extracting and summarizing opinions expressed in text data, including the sentiment, subjectivity, and intensity of the opinions.

Emotion detection: This involves identifying the emotions expressed in text data, such as anger, joy, sadness, or surprise.

Techniques and Tools for Sentiment Analysis
There are several techniques and tools available for performing sentiment analysis, ranging from rule-based methods to machine learning-based approaches. Some of the most popular techniques and tools for sentiment analysis include:

Lexicon-based methods: These involve using pre-defined dictionaries or lexicons of words and phrases with known sentiment polarity (e.g., positive, negative, or neutral) to classify the sentiment of text data.

Rule-based methods: These involve using a set of predefined rules or patterns to classify the sentiment of text data, such as detecting negations, intensifiers, or emoticons.

Machine learning-based methods: These involve training a machine learning model on a labeled dataset of text data with known sentiment polarity, and then using this model to classify the sentiment of new text data.

Deep learning-based methods: These involve using neural networks with multiple layers to learn representations of text data and classify its sentiment.

Practical Examples and Code Snippets

To demonstrate how to perform sentiment analysis on your own text data, we will use some code snippets in Python, along with some popular libraries for natural language processing and machine learning.

Sentiment Classification with TextBlob
TextBlob is a popular Python library that provides a simple and easy-to-use API for natural language processing tasks, including sentiment analysis. To perform sentiment classification with TextBlob, we can use the 'sentiment' method, which returns a tuple of two values: polarity, which ranges from -1 to 1, indicating the sentiment polarity of the text (negative, neutral, or positive); and subjectivity, which ranges from 0 to 1, indicating the degree of subjectivity of the text.
Example code snippet that demonstrates how to perform sentiment classification with TextBlob:

from textblob import TextBlob

text = "I really love this product! It's amazing!"

blob = TextBlob(text)

print("Sentiment polarity: ", blob.sentiment.polarity)
print("Sentiment subjectivity: ", blob.sentiment.subjectivity)

Sentiment Classification with NLTK
NLTK (Natural Language Toolkit) is another popular Python library for natural language processing tasks, including sentiment analysis. To perform sentiment classification with NLTK, we can use the NaiveBayesClassifier.
Example code snippet that demonstrates how to perform sentiment classification with NLTK:

import nltk
from nltk.sentiment import SentimentIntensityAnalyzer

nltk.download('vader_lexicon')

text = "I really love this product! It's amazing!"

sia = SentimentIntensityAnalyzer()

sentiment_scores = sia.polarity_scores(text)

print("Sentiment scores: ", sentiment_scores)

Aspect-Based Sentiment Analysis with Gensim
Gensim is a popular Python library for topic modeling, text analysis, and similarity detection. To perform aspect-based sentiment analysis with Gensim, we can use the 'LdaModel' and 'CoherenceModel' classes, which implement a probabilistic model of text data and a measure of the coherence of the topics, respectively.
Example code snippet that demonstrates how to perform aspect-based sentiment analysis with Gensim:

import gensim
from gensim import corpora, models
from gensim.models.ldamodel import LdaModel
from gensim.models.coherencemodel import CoherenceModel

texts = [["camera", "picture", "quality", "poor"],
         ["battery", "life", "short"],
         ["price", "too", "high"],
         ["customer", "service", "excellent"]]

dictionary = corpora.Dictionary(texts)
corpus = [dictionary.doc2bow(text) for text in texts]

lda_model = LdaModel(corpus=corpus, id2word=dictionary, num_topics=2, passes=10)

coherence_model = CoherenceModel(model=lda_model, texts=texts, dictionary=dictionary, coherence='c_v')

coherence_score = coherence_model.get_coherence()

print("Coherence score: ", coherence_score)

Conclusion

Sentiment analysis is a fascinating and important field in natural language processing, with numerous applications in business, politics, social media, and more. In this article, we have explored the fundamentals of sentiment analysis, including the different types of sentiment analysis tasks, the most popular techniques and tools for sentiment analysis, and some practical examples and code snippets in Python that demonstrate how to perform sentiment analysis on your own text data.

By using the techniques and tools we have discussed, you can gain valuable insights from the opinions, attitudes, emotions, and sentiments expressed in text data, and use this information to make better decisions, improve customer satisfaction, monitor brand reputation, and more.