Twitter sentiment analysis

Analyzing Social Media Data in R

Vivek Vijayaraghavan

Data Science Coach

Lesson Overview

What is sentiment analysis?
Perform sentiment analysis on tweets
Interpret to understand people's feelings and opinions

Sentiment analysis

Retrieve information on perception of a product or brand
Extract and quantify positive, negative and neutral opinions
Emotions like trust, joy, and anger from the text

Sentiment analysis emotions

Significance of sentiment analysis

Customer perceptions influence purchasing decisions
Helps understand the pulse of what customers feel
Proactive approach to listen to the customer and engage directly

How sentiment analysis works

Pre-defined sentiment libraries to calculate scores
Trained and scored based on meaning or intent of words
Each word is scored based on its nearness to a positive or negative word
Same concept is extended to words expressing specific emotions

Sentiment analysis steps

Step1 in sentiment analysis

Sentiment analysis steps

Step2 in sentiment analysis

Sentiment analysis steps

Step3 in sentiment analysis

Sentiment analysis steps

Step4 in sentiment analysis

Extract tweets for sentiment analysis

# Extract tweets on galaxy fold
twts_galxy  <-  search_tweets("galaxy fold", n = 5000, 
                                lang = "en", include_rts = FALSE)

Perform sentiment analysis

# Perform sentiment analysis for tweets on galaxy fold
library(syuzhet)
sa.value <- get_nrc_sentiment(twts_galxy$text)

View sentiment scores

# View the sentiment scores
sa.value[1:5,1:7]

anger    anticipation    disgust    fear    joy    sadness    surprise
<dbl>       <dbl>         <dbl>     <dbl>  <dbl>    <dbl>       <dbl> 
0             0             0         0      0        0           0
1             0             0         0      0        0           0
1             1             0         2      1        1           1
0             0             0         1      0        0           0
0             0             0         0      0        0           0

Sum of sentiment scores

# Calculate sum of sentiment scores
score <- colSums(sa.value[,])

Data frame of sentiment scores

# Convert to data frame
score_df <- data.frame(score)

# View the data frame
score_df

               score
               <dbl>
anger           211            
anticipation    825            
disgust         214            
fear            253            
joy             412            
sadness         197            
surprise        315            
trust           641            
negative        487            
positive       1351

Data frame of sentiment scores

# Convert row names into 'sentiment' column
# Combine with sentiment scores
sa.score <- cbind(sentiment = row.names(score_df), 
                  score_df, row.names=NULL)

Data frame of sentiment scores

# View data frame with sentiment scores
print(sa.score)

sentiment      score
<fct>         <dbl>
anger           211            
anticipation    825            
disgust         214            
fear            253            
joy             412            
sadness         197            
surprise        315            
trust           641            
negative        487            
positive        1351

Plot and visualize sentiments

Plot and visualize sentiments using ggplot()

# Plot the sentiment scores
ggplot(data = sa.score2, aes(x = sentiment, y = score, 
       fill = sentiment)) +
       geom_bar(stat = "identity") +
       theme(axis.text.x = element_text(angle = 45, hjust = 1))

Visualize the sentiments

Let's practice!

Analyzing Social Media Data in R