SOCIAL MEDIA SENTIMENT ANALYTICS

Business Objectives

Client wanted to achieve following key objectives:

Measure and manage their online reputation and customer interactions across multiple social media platforms
Measure effectiveness of digital marketing campaigns and their online customer engagement initiatives

V2’s team developed a structured approach to setup connectors to multiple social media platforms to collect the real-time data.
We extracted essential textual and statistical data from multiple data sources. Then ingested and merged social, customer and campaign data.
Thereafter we implemented a robust process that standardized and pre-processed the mined textual data. We then performed various pre-processing and core mining techniques which included:
- Sentence splitting
- Noun phrase chunking
- Lemmatization
- Stemming
And NLP tasks such as:
- POS tagging
- Tokenization
- Parsing
- Named entity recognition
- Information
- Relation extraction
This helped us derive key topics and associated context which were further modeled to arrive at various NLP measures such as relevance, influence and sentiment polarization.
The Analytics team assigned a sentiment in three categories – positive, negative, neutral. Data analysis and validation of the model was performed by verifying various trends and patterns. Visual dashboards of key parameters highlighted the pre & post trends and patterns across different segments.

Our solution helped the client first get a sense of how the brand was perceived in the social world
Sentiment analysis helped gauge customer acceptance of user campaigns
Pre- & Post campaign analytics helped the business stakeholders take informed decisions for future promotions
Increased sales of brands through targeted promotional strategies
Quicker response times to customer posts, queries, and comments on social channels