IS428 2018 19T1 Group11 Proposal

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RESEARCH PAPER

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PROBLEM AND MOTIVATION

Despite Grab’s strong presence within the South East Asian rideshare market following the acquisition of Uber in March of this year, the growing number of players within the different fields in which Grab operates in incentivises the company to adopt non-traditional methods to improve its business operations.

While it is important to fulfil the bottomline, a huge determinant of the company’s success is rooted to public’s perception and Grab’s positioning in the markets. As such, this project aims to create a systematic method in which Grab can use to get an idea of the public’s sentiment on their product and the company image.

The NLP algorithm used by Grab to describe the public’s sentiments follows the Latent Dirichlet Allocation (LDA) model, a generative statistical model that allows sets of observations to be explained by unobserved groups, or topics, that explain why some parts of the data are similar. With this model, Grab hopes to identify latent topics of interest, and be able to get an understanding of the public’s perception of the topic. Grab can then make use of this information to address the inadequacies in their business practices more directly, create more effective marketing campaigns and improve on their business operations to build a stronger overall brand image.

OBJECTIVES

The objective of the visualization is to bridge the gap between the analytics and the business teams. While the findings from the LDA model might be intuitive for those from the analytics team, the business users may find it difficult to internalize the findings. Hence, our job is to create a scalable way to present these findings to the business users, such that it is easy to understand.

DATA SOURCE

Data Source
Data used is obtained from web scraping of various social media platforms such as Instagram, Twiiter, Reddit and Google Playstore.
The data set consists of 9000 comments that were scraped and collected.

Data Attributes
The following is a snapshot of the data collected, and a description of the data attributes:

Figure 1: Comments Dataset

Data Attributes	Description of attributes
Document	Comments scraped may consist of more than a sentence each. They are separated and identified by documents. Hence, a document represents a sentence of comment.
Dominant_Topic	Dominant topic refers to the topic that the document will most likely be sorted into.
Topic_Perc_Contrib	The probability that the comment will be found in the topic amongst all other comments with similar keywords.
Keywords	Keywords that belong in each of the topics.
Text	Words in each comment after the removal of stop words (eg. the, is, to, on etc).
Original_Comment	Original comment sentence that was scraped.
Comment_Date	Date that the comment was posted.

BACKGROUND SURVEY OF RELATED WORKS

Related Works	What We Can Learn
Grab Traffic Trends "Insert Image here" Source: "Insert source here"	Learning 1 Learning 2 Learning 3
Another Graph "Insert Image here" Source: "Insert source here"	Learning 1 Learning 2 Learning 3

STORYBOARD

Sketches	Description of Approach
Sketch 1 "Insert Image here" Source: "Insert source here"	Approach 1 Approach 2 Approach 3
Sketch 2 "Insert Image here" Source: "Insert source here"	Approach 1 Approach 2 Approach 3

PROPOSED VISUALISATION

???? Write what???? halps

KEY TECHNICAL CHALLENGES

• Challenge 1
• Challenge 2
• Challenge 3

PROJECT TIMELINE

"Insert Gantt Chart"

REFERENCES

"Insert Links and Description"

COMMENTS

Something