Mining feature-opinion pairs and their reliability scores from web opinion sources

transcript

MINING FEATURE-OPINION PAIRS AND THEIR RELIABILITY

SCORES FROM WEB OPINION SOURCES

Presented by Sole

A. Kamal, M. Abulaish, and T. AnwarInternational Conference on Web

Intelligence, Mining and Semantics (WIMS) 2012

Introduction Opinion Data: user-generated content

Opinion SourcesForums Discussion Groups Blogs

Customer Manufacturer

Introduction Problems with Reviews

Information OverloadTime Consuming Solution

Approach to Extract feature-opinions pairs from reviews Determine the reliability score of each pair

Biased Information

Related Work Relatively new area of study

Information Retrieval Classification of positive/negative reviews

NLP, text mining, probabilistic approaches Identify patterns on text to extract attribute-

value pairs

Proposed Approach Architecture of the system

Pre-processing Review crawler

Noisy reviews are removed Eliminate reviews created with no purpose or to

increase/decrease the popularity of a product Markup language is filtered Remaining content is divided into

manageable sizes Boundaries are determined based on heuristics,

e.g., granularity of words, stemming, synonyms

Document Parser Text Analysis

Assigns Part-Of-Speech (POS) tags to each word

Converts each sentence into a set of dependency relations between pairs of words

Facilitate information extractionNoun Phrases

Adjectives

Adverbs

Product features

Degree of expressiveness of

opinions

Opinions

Feature and Opinion Learner Feature-opinion learner

Analyzes dependency relations generated by a document

Generates all the possible information components from the documents Information component: <f, m, o>

f refers to feature m refers to modifier o refers to an opinion

Feature and Opinion Learner Rule 1

In a dependency relation R, if there exist relationships nn(w1,w2) and nsubj(w3,w1) such that POS(w1)=POS(w2)=NN*, POS(w3)=JJ* and w1,w2 are not stop-words, or if there exists a relationship nsubj(w3,w4) such that POS(w3)=JJ*, POS(w4)=NN* and w3,w4 are not stop-words, then either (w1,w2) or w4 is considered as a feature and w3 as an opinion.

In a dependency relation R, if there exist relationships nn(w1,w2) and nsubj(w3,w1) such that POS(w1)=POS(w2)=NN*, POS(w3)=JJ* and w1,w2 are not stop-words, or if there exists a relationship nsubj(w3,w4) such that POS(w3)=JJ*, POS(w4)=NN* and w3,w4 are not stop-words, then either (w1,w2) or w3 is considered as a feature and w4 as an opinion. Thereafter, the relationship advmod(w3,w5) relating w3 with some adverbial word w5 is searched. In case of presence of advmod relationship, the information component is identified as <(w1,w2) or w4,w5,w3> otherwise <(w1,w2) or w4,-,w3>.

In a dependency relation R, if there exist relationships nn(w1,w2) and nsubj(w3,w1) such that POS(w1)=POS(w2)=NN*, POS(w3)=VB* and w1,w2 are not stop-words, or if there exist a relationship nsubj(w3,w4) such that POS(w3)=VB*, POS(w4)=NN* and w4 is not a stop-word, then we search for acomp(w3,w5) relation. If acomp relationship exists such that POS(w5)=JJ* and w5 is not a stop-word then either (w1,w2) or w4 is assumed as the feature and w5 as an opinion. Thereafter, the modifier is searched and the information component is generated in the same way as in Rule-2.

In a dependency relation R, if there exist relationships nn(w1,w2) and nsubj(w3,w1) such that POS(w1)=POS(w2)=NN*, POS(w3)=VB* and w1,w2 are not stop-words, or if, there exist a relationship nsubj(w3,w4) such that POS(w3)=VB*, POS(w4)=NN* and w4 is not a stop-word, then we search for dobj(w3,w5) relation. If dobj relationship exists such that POS(w5)=NN* and w5 is not a stop-word then either (w1,w2) or w4 is assumed as the feature and w5 as an opinion.

In a dependency relation R, if there exists a amod(w1,w2) relation such that POS(w1)=NN*, POS(w2)=JJ*, and w1 and w2 are not stop-words then w2 is assumed to be an opinion and w1 as an feature.

In a dependency relation R, if there exists relationships nn(w1,w2) and nsubj(w3,w1) such that POS(w1)=POS(w2)=NN*, POS(w3)=VB* and w1,w2 are not stop-words, or if there exists a relationship nsubj(w3,w4) such that POS(w3)=VB*, POS(w4)=NN* and w4 is not a stop-word, then we search for dobj(w3,w5) relation. If dobj relationship exists such that POS(w5)=NN* and w5 is not a stop-word then either (w1,w2) or w4 is assumed as the feature and w5 as an opinion. Thereafter, the relationship amod(w5,w6) is searched. In case of presence of amod relationship, if POS(w6)=JJ* and w6 is not a stop-word, then the information component is identified as <(w1,w2) or w4, w5,w6> otherwise <(w1,w2) or w4, w5,->.

Feature and Opinion Learner Example

Consider the following opinion sentences related to Nokia N95The screen is very attractive and brightThe sound some times comes out very clearNokia N95 has a pretty screenYes, the push mail is the “Best” in the

business

Reliability Score Generator Reliability Score

Removes noise due to parsing errors Addresses contradicting opinions in reviews

Reliability Score Generator HITS Algorithm

A higher score value for a pair reflects a tight integrity of the 2 components in a pair

The Hubs and Authority scores are computed iteratively The feature score is calculated using the term frequency

and inverse sentence frequency in each sentence of the document

𝐴𝑆 (𝑡+1 ) (𝑑𝑘 )= ∑𝑝 𝑖𝑗∈𝑉 𝑝

𝑊𝑘𝑖𝑗×𝐻𝑆(𝑡 )𝑝𝑖𝑗

𝐻𝑆( 𝑡+1) (𝑝𝑖𝑗 )= ∑𝑑𝑘∈𝑉 𝑑

𝑊𝑘𝑖𝑗×𝐴𝑆(𝑡 )𝑑𝑘

Based on feature score and opinion

Authority

Reliability Score Generator Pseudocode

1 G := set of pages 2 for each page p in G do 3 p.auth = 1 // p.auth is the authority score of the page p 4 p.hub = 1 // p.hub is the hub score of the page p 5 function HubsAndAuthorities(G) 6 for step from 1 to k do // run the algorithm for k steps 7 for each page p in G do // update all authority values first 8 p.auth = 0 9 for each page q in p.incomingNeighbors do // p.incomingNeighbors is the set of pages that link to p 10 p.auth += q.hub 11 for each page p in G do // then update all hub values 12 p.hub = 0 13 for each page r in p.outgoingNeighbors do //p.outgoingNeighbors is the set of pages that p links to 14 p.hub += r.auth

Experimental Results Dataset

400 review

4333 noun (or verb) and adjective

1366 candidate features

obtained after filtering

Sample list of extracted features, opinions, modifiers

Experimental Results Metrics

True positive (TP): number of feature-opinion pairs that the system identifies correctly

False positive (FP): number of feature-opinion pairs that are falsely identified by the system

False negative (FN): number of feature-opinion pairs that the system fails to identify

𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛=𝑇𝑃

𝑇𝑃+𝐹𝑃 𝑅𝑒𝑐𝑎𝑙𝑙= 𝑇𝑃𝑇𝑃+𝐹𝑁

𝐹−𝑀𝑒𝑎𝑠𝑢𝑟𝑒=2×𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛×𝑅𝑒𝑐𝑎𝑙𝑙𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛+𝑅𝑒𝑐𝑎𝑙𝑙

Experimental Results Feature and Opinion Learner

Precision: 79.3% Recall: 70.6% F-Measure: 74.7% Observations

Direct and strong relationships between noun and adjectives cause non-relevant feature-opinion pairs

Lack of grammatical correctness in reviews affects the results yielded by NLP parsers

Recall values lower than precision indicates the inability of the systems to extract certain feature-opinion pairs correctly

Experimental Results Sample results for different products

Observations Lack of variations on metric values indicates

the applicability of the proposed approach regardless of the domain of the review documents

Experimental Results Reliability Score Generator

Top-5 hub scored feature and opinion pairs and their reliability scores

Sample feature-opinion pairs along with their hub and reliability scores

Conclusions

Future Work Refine rules to improve precision and

identify implicit features Handle informal text common in reviews

Reviews + Rules

Feature-pinion pairs

Hits Algorithm

Reliability scores

Mining feature-opinion pairs and their reliability scores from web opinion sources

Documents