aspect detection in sentiment analysis

1. 1
Aspect identification &
opinion polarity
detection

2. 2
What is an Aspect??
● A particular part or feature of something.
It has a strong
story
All of the shots are well
composed
The action seem more
real
one of the
best works of
cinematic art
The movie is amazing.
Movie
The music is simply
beautiful

3. 3
Detect aspects..
• Aspects
• Explicit
• E.g. : The touch screen is really cool
• Implicit
• E.g. : The phone is so heavy
• There two types of opinion (Liu, 2010):
• Regular Opinion
• Sentiment/opinion expressions
• Comparative opinion
• E.g. : “Camera of iphone 5 is better
than Samsung galaxy s5”
We focus on regular explicit Regular opinion

4. 4
Web Reviews
Aspect
Identification
Opinion detection
Polarity
extraction
Score
Calculation
Data Collection
Figure1: Overall System Diagram

5. 5
Data Collection
URL
Crawler
Id of movies
IMDB
id Extracting movie name
Get reviews for each movie
Get Movie Charecter information
Website : http://www.imdb.com
Year : 2014
Total Movie Downloaded : 9435
Movies with reviews : 2185
Removed movies : 7250
Filter files containing reviews
Figure2: Data Collection

6. 6
Aspect Identification
This is one of the best works of cinematic art of all time. Each scene is artfully
composed, the actors' performances are subtle, powerful and evoke far more than
than their words alone convey. The tongue-in-cheek but so-serious performances
are dead-on. I have seen this film at least a dozen times and never tire of it; in fact,
I appreciate it more each time I see it. Its economy of dialog, composition, and plot
is the soul of this wonderful piece. A fresh plot, the artful placement of the spare
and haunting musical score, the stark cinematography, masterful directing and
first-rate performances make this one a real winner!
How to identify???

7. 7
Proper noun extraction
Tagged output
POS Tagging using Standford Tagger API
Extract proper noun
Tokenizing
Figure 3: Proper Noun Extraction

8. 8
This_DT is_VBZ one_CD of_IN the_DT best_JJS works_NNS of_IN cinematic_JJ art_NN
of_IN all_DT time_NN ._. Each_DT scene_NN is_VBZ artfully_RB composed_VBN ,_, the_DT
actors_NNS '_POS performances_NNS are_VBP subtle_JJ ,_, powerful_JJ and_CC evoke_VB
far_RB more_JJR than_IN than_IN their_PRP$ words_NNS alone_RB convey_VBP ._.
The_DT tongue-in-cheek_JJ but_CC so-serious_JJ performances_NNS are_VBP dead-
on_JJ ._. I_PRP have_VBP seen_VBN this_DT film_NN at_IN least_JJS a_DT dozen_NN
times_NNS and_CC never_RB tire_NN of_IN it_PRP ;_: in_IN fact_NN ,_, I_PRP
appreciate_VBP it_PRP more_RBR each_DT time_NN I_PRP see_VBP it_PRP ._. Its_PRP$
economy_NN of_IN dialog_NN ,_, composition_NN ,_, and_CC plot_NN is_VBZ the_DT
soul_NN of_IN this_DT wonderful_JJ piece_NN ._. A_DT fresh_JJ plot_NN ,_, the_DT artful_JJ
placement_NN of_IN the_DT spare_JJ and_CC haunting_NN musical_JJ score_NN ,_, the_DT
stark_JJ cinematography_NN ,_, masterful_JJ directing_NN and_CC first-rate_JJ
performances_NNS make_VBP this_DT one_CD a_DT real_JJ winner_NN !_.
Is it efficient???

9. 9
This_DT is_VBZ one_CD of_IN the_DT best_JJS works_NNS of_IN cinematic_JJ art_NN
of_IN all_DT time_NN ._. Each_DT scene_NN is_VBZ artfully_RB composed_VBN ,_, the_DT
actors_NNS '_POS performances_NNS are_VBP subtle_JJ ,_, powerful_JJ and_CC evoke_VB
far_RB more_JJR than_IN than_IN their_PRP$ words_NNS alone_RB convey_VBP ._.
The_DT tongue-in-cheek_JJ but_CC so-serious_JJ performances_NNS are_VBP dead-
on_JJ ._. I_PRP have_VBP seen_VBN this_DT film_NN at_IN least_JJS a_DT dozen_NN
times_NNS and_CC never_RB tire_NN of_IN it_PRP ;_: in_IN fact_NN ,_, I_PRP
appreciate_VBP it_PRP more_RBR each_DT time_NN I_PRP see_VBP it_PRP ._. Its_PRP$
economy_NN of_IN dialog_NN ,_, composition_NN ,_, and_CC plot_NN is_VBZ the_DT
soul_NN of_IN this_DT wonderful_JJ piece_NN ._. A_DT fresh_JJ plot_NN ,_, the_DT
artful_JJ placement_NN of_IN the_DT spare_JJ and_CC haunting_NN musical_JJ score_NN
,_, the_DT stark_JJ cinematography_NN ,_, masterful_JJ directing_NN and_CC first-rate_JJ
performances_NNS make_VBP this_DT one_CD a_DT real_JJ winner_NN !_.
How to avoid these???

10. 10
Aspect Vector
Possible aspect terms
with categories
Genre Related comedy, funniness, joke, jokes, joking, funny,
funnier, funniest, entertaining, entertainment,
action , actions, romance, thriller, horror, comics,
humor, amusement, amusements, comic, thrilling,
thrill, thrilled
Acting act, acting , acted, acts, portray, portrays, portrayed,
portraying, villain, perform, performed, performance,
performing, play, plays, playing, played, casting, cast,
casts, hero, lead, leading, actor, actors, actress, actresses,
artist, artists, cast, delivered, character, role, personality,
personalities, acted, heroine, antihero, anti-hero, star, artist
Direction film maker, film making, filmed, filming, director,
directed, directional, direction, directing
Story script, scripts, storyline, story, plot, plots, writer,
writers, writing, end, ending, storytelling, revenge,
betrayal, twist, drama, date, romance, dialogs,
dialog, narrative, narration, narrated, narrates,
dialogue, climax, play
Music sound, sounds, song, songs, sung, singing, sings,
sing, lyric, lyrics, singer, audio, audios, music,
music’s, musical, title track
Choreography choreographer, choreographed, dance, dancer,
danced, dances, cinematic, composition
Cinematograp
hy
cinematic, sense, senses, scene, scenes, scenery,
sceneries, set, stage, stages, animation, animate,
animated, violence, screenplay, special effect,
stunt, shot, shots, visual, visuals, props, camera,
graphic, graphics, lifelike, realistic, editing, post
production, special effects, filmic, visuals
Overall movie, film, flick, award, awards, blockbuster,
award winning, picture, show, nomination, winner,
oscar
Table 1: Aspect Vector

11. 11
Extracted proper nouns Aspect vector
If nouns is in the vector
If exist
Read Categories
Terms in each category
Select aspect
Write to file
Figure 4: Aspect Vector method

12. 12
Aspects identified:
works
art
scene
actors
words
Ihave
film
dozen
tire
fact
time
economy
dialog
composition
soul
piece
plot
placement
haunting
score
cinematography
directing
performances
winner
Aspect matched:
actors
directing
dialog
plot
composition
scene
cinematography
film
winner
Problems:
Domain Specific
May not contain all the terms

13. 13
• The Point wise Mutual Information (PMI)
between two words, word1 and word2, is
defined as follows (Church & Hanks, 1989):
PMI(word1, word2) = log2
• How to calculate probability??
• Using PMI-IR
Point wise Mutual Information
(PMI)
p(word1 & word2)
p(word1) p(word2)

14. 14
Aspect Categories
Query to google
Get number of hits
(only category term)
Extracted proper noun
Query to google
Get number of hits
(only noun term)
Combine terms '_' movies
Query to google
Get number of hits
(Combined score)
Calculate PMI
>
threshold
Combine both
terms with '_'
Get number of hits
(Combined score)
Calculate PMI
Get maximum PMI
Classify into the category
Figure 5: PMI method

15. 15
PMI-IR
• Issuing queries to a search engine and noting the
number of hits (matching documents).
• PMI(word1, word2) = log10 hits(word1 & word2)
hits(word1) hits(word2)

16. 16
Categories:
movie_type
Acting
Direction
Story
Music
Choreography
Cinematography
movie
Get Maximum PMI
Check for threshold
value
Extract term
Group into the category
Figure 6: Classification of term

17. 17
Read Categories
Extracted proper
nouns
Aspect vector
If nouns is in
the vector
Each Terms in
every category
If not exist
Query to
google
Get number of hits
(only noun term)
Combine both
terms using '_'
Query to
google
Get number of hits
(Combined score)
Calculate PMI
Query to
google
Get number of hits
(only category term)
Select aspect
Write
to file
If exist
PMI-Aspect Vector
Figure 7a: PMI-Aspect vector method

18. 18
PMI
Get Maximum PMI of each Category
> Threshold
Extract categories satisfying threshold
Get Maximum
Classify into that category
Write
to file
Figure 7b: PMI-Aspect vector method

19. 19
References

20. 20

21. 21