18 - SENTIMENT ANALYSIS

Question 1

Sentiment analysis in marketing (*)

Answer 1

people’s “opinions”

about a product/ company

• by analysing public’s “comments”

on various “online platforms”.

Online statements or customer comments are turned into categorical data (like “positive”, “negative” or “neutral”), and summarised to give a manager a bird’s eye view of how the general public is responding to their brand or product.

(Kỹ thuật phân tích quan điểm)

Question 2

Advantages in Using Sentiment Analysis

Answer 2

Gauging public sentiment is facilitated by the development of more sophisticated tools
Traditional research tools: prohibitively expensive & time-consuming + more prone to a degree of human error

Sentiment analysis can help reveal ‘influencers’ for a brand or a product. This could be critical when people are seeking sincere advice on matters which are important to them.

identify “hate speech online”, especially when it involves the brand or any of its key values. Sentiment analysis is useful in highlighting such a trend, even if it is only limited to a certain group. Strategies to navigate through such “consumer negativity” in the digital world is critical.

Question 2

Steps in sentiment analysis

Answer 2

1. Detecting sentiment:
   quick overview of the overall opinion of customers; growing through online comments and “extracting opinionated data” - eg. I love this!

2. Categorising sentiment and identifying the intensity:
   detecting whether the sentiment is “positive, negative, or neutral”
   (very positive
   somewhat positive
   neutral
   somewhat negative
   very negative)

3. Mixed Connotation:
   Sometimes, the text contains “mixed or ambivalent” opinions, for example, “staff was very friendly but we waited too long to be served”. One would need to separately interpret such statements which might be difficult for a simple machine-based program to code and analyse

Question 3

Limitations of sentiment analysis

Answer 3

“Machine-dependent” sentiment analysis is based on the way people use language to express their opinions.
A word’s meaning in the “dictionary could be very different from the way people use” it in everyday conversations.
Sentiment analysis may run into issues when online users utter phrases to “display sarcasm”

It is still felt that sentiment analysis is most effective when it is used with large and numerous data sets. “Small businesses” may find that there is “not much data available” for their products/services which can be effectively analysed

(Moreover, sentiment analysis is not a one-off activity. It has to be integrated into a firm’s information-gathering strategy.)
To get real value out of sentiment analysis tools, one needs to “analyse large quantities of textual data on a regular basis”

Question 4

Lexicon-based Sentiment Analysis and Valence

Answer 4

StSc = (number of positive words - number of negative words)/ total number of words

Positive: +1
Neutral: 0
Negative: -1