Sentiment Analysis with Machine Learning

Posted by OodlesAI on December 23rd, 2019

What Is Sentiment Analysis?

It is the process of determining if a piece of writing is positive, negative or neutral. This system for text analysis combines natural language processing and machine learning techniques to assign weighted sentiment scores to the entities, topics, themes, and categories within a sentence or phrase. Sentiment analysis and artificial intelligence services enable data analysts to analyze public opinions, conduct in-depth market research, evaluate brand reputation, and enhance customer experiences. Moreover, businesses may integrate third-party sentiment analysis APIs into their database management systems for different platforms to extract customer insights.                                                                                                                    

How does sentiment analysis work?

Text documents follows the following process:

1. First of all Break each text document down into its component parts (sentences, phrases, tokens, and parts of speech).
2. Identify each sentiment-bearing phrase and component.
3. Then Assign a sentiment score to each phrase and component (-1 to +1).
4. Optional: Combine scores for multi-layered sentiment analysis.

Sentiment Analysis Algorithms

There can be many methods and algorithms to implement this systems, which can be classified as:

• Automatic systems that work on machine learning techniques to learn from data.
• Hybrid systems combine both rule-based and automatic approaches.
• Rule-based systems perform sentiment analysis based on a set of manually crafted rules.

Sentiment Analysis Use Cases & Applications

• Sentiment Analysis in Social Media Monitoring

In today’s day and age, brands of all shapes and sizes have meaningful interactions with customers, leads, and even competition on social networks like Facebook, Twitter, and Instagram. Most marketing departments are already tuned into online mentions as far as volume –they measure more chatter as more brand awareness. Nowadays, however, we can take a step deeper. By sentiment analysis on social media, we can get incredible insights into the quality of conversation that’s happening around a brand.

1. Analyze tweets and/or Facebook posts over a period of time to see sentiment a particular audience.
2. Run this analysis on all social media mentions to your brand and automatically categorize it by urgency.
3. Automatically route social media mentions to team members best fit to respond.
4. Automate any or all of these processes.
5. Use analytics to gain deep insight into what’s happening across on social media channels.

• Sentiment Analysis in Brand Monitoring

Not only do brands have a wealth of information available on social media, but they also can look more broadly across the internet to see how people are talking about them online. Instead of focusing on specific social media platforms such as Facebook and Twitter, we can target mentions in places like news, blogs, and forums –again, looking at not just the volume of mentions, but also the quality of those mentions.

How Sentiment Analysis Can Be Used

1. Analyze news articles, blog posts, forum discussions, and other texts on the internet over a period of time to see the sentiment of a particular audience.
2. Automatically categorize the urgency of all online mentions to your brand via sentiment analysis.
3. Automatically alert designated team members of online mentions that concern their area of work.
4. Automate any or all of these processes.
5. Better understand a brand’s online presence by getting all kinds of interesting insights and analytics.

• Sentiment Analysis in Customer Feedback

Social media & brand monitoring offer us immediate, unfiltered, invaluable information on customer sentiment. In parallel vein run two other troves of insight –surveys and customer support interactions. Teams often look at their Net Promoter Score (NPS), but we can also apply these analyses to any type of survey or communication channel that yields textual customer feedback.

In numerical survey data it is easily aggregated and assessed, but we want that same ease with the “why” answers as well. Regular NPS score simply gives a number, without the additional context of what it’s about and why the score landed there. However, AI-powered sentiment analysis provides an automated description of the text including the “what” and “why”. 

1. Analyze aggregated NPS or other survey responses.
2. Analyze aggregated customer support interactions.
3. Track customer sentiment about specific aspects of the business over time. It adds depth to explain why the overall NPS score might have changed, or if specific aspects have shifted independently.