Opporture Lexicon

Sentiment Analysis

Sentiment Analysis is the interpretation of the emotional tone from a written text using advanced text analysis techniques. These techniques are categorized into positive, neutral, or negative sentiments with the help of Natural Language Processing or NLP.

Sentiment Analysis is a vital aspect of Artificial Intelligence and Machine Learning. This process relies on AI’s ability to comprehend human language and assess emotions from written text.

Applications of Sentiment Analysis: How & Where it is Used

Sentiment Analysis is used to:

• Gain customer insights

Using Sentiment Analysis, organizations gain deeper insights into customer feedback from comments and reviews on social media platforms, forums, and review sites. This in-depth analysis enables companies to make data-driven decisions to improve customer experience with their products and services.

• Strengthen marketing strategies

Sentiment Analysis evaluates customers’ responses to new products, marketing campaigns, and advertisements. The real-time feedback decodes the efficacy of their marketing strategies, allowing them to modify and optimize their efforts for better outcomes.

• Augment customer support

Sentiment Analysis is critical in maintaining a competitive edge in business. Automatic analysis of customer feedback, complaints, and queries using NLG identifies pitfalls in customer interactions. Companies leverage this customer sentiment analysis to prioritize critical issues and align their responses for optimized customer experience.

• Maintain brand reputation

Businesses rely on Sentiment Analysis to reinforce their brand reputation. The technology scans and evaluates online brand mentions in number and volume and categorizes them into positive, negative, or neutral tones. The analysis helps strengthen public relations and promptly address negative sentiments.

• Analyze market trends

Sentiment Analysis uncovers new market trends and customer behavioral changes by diving deep into online news articles, blogs, and social media content. Topic, product, or service-based Sentiment Analysis facilitates robust decisions regarding future strategies and product developments.

FAQs

1. How does Sentiment Analysis operate?

There are two approaches to Sentiment Analysis:

Machine Learning approach where the text classifier is trained on human-labeled data.
Lexicon-based approach, which assigns a sentiment score for each work and combines them to form a common sentiment for the text.

2. What are the types of Sentiment Analysis?

Sentiment Analysis is of three types:

Binary sentiment analysis- Segregation of text into positive or negative
Multi-class sentiment analysis- Text classification into neutral, positive, and very positive.
Granular sentiment analysis- Allocate scores based on negative or positive polarity.

3. What are the pros and cons of ML-based Sentiment Analysis?

When trained with domain-specific datasets, Machine Learning-based Sentiment Analysis is accurate and robust. However, the challenges with this Sentiment Analysis include:

Inaccurate results if the data is biased
Requires in-depth training in annotation
Expensive endeavor requiring expertise

4. What are the four steps of sentiment analysis?

Step 1: Data collection

Collation and annotation of data from repositories, social media, reviews, manually uploaded .csv files, or through live APIs.

Step 2: Data processing

Processing of text, audio, image, and video-based data using methods like:

Audio transcription
Image text recognition
Caption extraction
Logo identification
Emoji capturing

Step 3: Data analysis

Using labeled datasets for model training, creating customized tags for themes, classifying topics, and analyzing sentiment scores for themes.

Step 4: Data visualization

Transforming analyzed data into simple charts and graphs to aid task creation and identification of focus areas.

5. What are the uses of Sentiment Analysis?