This document presents an analysis of reviews from IMDb for the Hobbit series, focusing on web crawling, sentiment analysis, and the creation of a term document matrix. Key findings indicate varying levels of negative and positive ratings across the three films, with notable differences in the polarity of reviews and influential terms affecting ratings over the years. The analysis leverages techniques like latent semantic analysis to interpret sentiments and highlight the words most impactful in shaping viewer perceptions.

1. TEXT ANALYTICS
Analysis of reviews fetched
from IMDB for Hobbit Series
1
Submitted By :-
Amrapalli Karan
Kamalika Some
Krishanu Mukherjee
Somenath Sit

2. Objective
• Web Crawling from IMDB for 3 sequels of The Hobbit.
• Creation of Term Document Matrix and WordCloud
• Dimension Reduction using Latent Semantic Analysis
• Influencing Words in Ratings
• Comparison of sentiments expressed in reviews and ratings
given
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3. Web Crawling
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4. Cleaning of TDM
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TDM Dictionary
Final TDM
Filtered TDM
Excluded few common but unnecessary
words like "hobbit", "film", "movie",
"movies“ etc.
Dictionary with
common english
words

5. Dominating Words in TDM
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Hobbit - 2012 Hobbit - 2013 Hobbit - 2014

6. Dimension Reduction using LSA
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TK DK SK

7. Important Variables in TK matrix
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Built a model with “satisfaction” as response variable, to find out which variable are having
more power in predicting the “Ratings
satisfaction=ifelse(Ratings<5,"Dissatisfied",
ifelse(Ratings<7,"Satisfied","Impressed!"))

8. Dimension Reduction using LSA
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• Plotted variable importance with Scree Plot.
• Take optimal no of variables (documents) to filter DK matrix.

9. DK matrix with important variables
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10. For Hobbit-2012
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• Story ,book, like these words
are having deciding power in
“Ratings”.
• People talked more about the
book and the story line.

11. For Hobbit-2013
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• Series ,good , great, story
these words are having
deciding power in “Ratings”.
• In 2013 also viewers were only
impressed with the story,
battles etc.

12. For Hobbit-2014
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• Beside like, good; bad, story
these words are also having
deciding power in “Ratings”.
• Along with the good words,
some negative words have
been used here.
• Story, book these things are not
that effecting in comparison
with previous sequels.

13. Sentiment Analysis
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• A basic task in sentiment analysis is classifying
the polarity of a given text at the document,
sentence, or feature/aspect level — whether
the expressed opinion in a document, a
sentence is positive, negative, or neutral.
• We performed sentiment analysis (polarity) on
the movie reviews of Hobbit and its sequels .
• We used R for the analysis.

14. Average Ratings
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3.65
2.52
4.13
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
Hobbit :An Unexpected Journey (2012) For Hobbit: The Desolation of Smaug
(2013)
For Hobbit: The Battle of the Five
Armies (2014)

15. Key Findings………
• For Hobbit :An Unexpected Journey (2012),
97% of the negative ratings had a negative
polarity for the corresponding reviews, while
78% of the positive ratings had a positive
polarity for the corresponding reviews.
• 76.5% of the ratings were negative.
• The average polarity of the reviews was
(0.012).

16. Key Findings….(contd)…
• For Hobbit: The Desolation of Smaug (2013),
85% of the negative ratings had a negative
polarity for the corresponding reviews, while
60% of the positive ratings had a positive
polarity for the corresponding reviews.
• 89% of the ratings were negative.
• The average polarity of the reviews was
(0.004).

17. Key Findings….(contd)…
• For Hobbit: The Battle of the Five
Armies (2014), only 3% of the negative ratings
had a negative polarity for the corresponding
reviews, while 100% of the positive ratings
had a positive polarity for the corresponding
reviews.
• 68% of the ratings were negative.
• The average polarity of the reviews was
(0.005).

18. Polarity Comparison - Region wise
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Hobbit 2012 Hobbit 2013
Hobbit 2014

19. Polarity Comparison – Region wise
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