In this section
Sentiment analysis is an automated process for analyzing text to interpret and extract subjective information. Through machine learning and text analysis, these algorithms can classify texts as positive, negative, or neutral, or identify the emotions within them. It helps a company understand the social sentiment surrounding its brand, product, or service.
Objectives
- Monitor brand mentions or reviews across different platforms
- Understand the main issues and complaints encountered by users
- Utilize information for customer behavior studies
In the era of social media, a single viral review can change the perception of an entire brand. By analyzing tweets, online reviews, and news articles at scale, business analysts gain actionable insights into how customers feel about their brands, products, and services. This allows potential issues to be addressed, improving the brand’s pain points.
What we do
Text messages posted by a company or any user on a social network or other platforms are analyzed.
How we do it
Data Collection
Depending on the study’s objectives, it may be necessary to interact with the APIs of the corresponding social networks or extract the specific messages for which the sentiment needs to be determined.
Text Processing
All comments or writings require preprocessing before subsequent analysis. This includes removing stop words, eliminating gender and plurals, reducing words to their lexemes, etc.
Model Application
In this phase, predictive classification models using artificial intelligence can be applied, or pre-built dictionaries containing a prior classification of words based on the sentiment they evoke can be used.