Computational Intelligence - February 2014 - 25
SenticNet
C&C
Levin++
SentiWorldNet
Analogical
Reasoning
Boxer
FRED
Machine Reader
Opinion Model Annotator
Sentiment Score
Computation Component
Topic and Opinion
Models Index
Sentiment Patterns
Learner
Topic Co-Reference
Resolution
Fuzzy Reasoner
Evolutionary Optimization
and Learning Mechanisms
Figure 4 The pipeline of components in Sentilo.
Drug_1 (N{Y}/N{Y}<1>) {_} 1 dealers_2 0
3,600_6 (N{Y}/N{Y}<1>) {_} 1 kilos_7 0
some_5 (NP[nb] {Y}/N{Y}<1>) {_} 1 kilos_7 0
colonel_11 (N{Y}/N{Y}<1>) {_} 1 said._12 0
the_10 ((NP{Y}\NP{Y}<1>) {_}/N{Z}<2>) {_} 2 said._12 0
the_10 ((NP{Y}\NP{Y}<1>) {_}/N{Z}<2>) {_} 1 narcotics,_9 0
of_8 ((NP{Y}\NP{Y}<1>) {_}/NP{Z}<2>) {_} 2 narcotics,_9 0
of_8 ((NP{Y}\NP{Y}<1>) {_}/NP{Z}<2>) {_} 1 kilos_7 0
lost_4 ((S[pt] {_}\NP{Y}<1>) {_}/NP{Z}<2>) {_} 2 kilos_7 0
have_3 ((S[dcl] {_}\NP{Y}<1>) {_}/(S[pt] {Z}<2>\NP{Y*}){Z}) {_} 2 lost_4 0
lost_4 ((S[pt] {_}\NP{Y}<1>) {_}/NP {Z}<2> {_} 1 dealers_2 0 ((S[dcl] {x}\NP{y}<28>) {x}/ (S[pt] {z}<29>
\NP{Y*}) {Z}) {x}
have_3 ((S[dcl] {_}\NP{Y}<1>) {_}/(S[pt] {Z}<2>\NP{Y*}){Z}) {_} 1 dealers_2 0
DrugWdrugWNNWI-NPW0WN/N dealersWdealersWNNSWI-NPW0WN haveWhaveWVBPWIVPW0W (S[dcl]\NP) / (S[pt]\NP) lostWlostWVBNWI-VPW0W (S[pt]\NP) / NP someWsomeWDTWI-NPW0WNP[nb]/N
3,600W3,600WCDWI-NPW0WN/N kilosWkilosWNNSWI-NPW0WN ofWofWINWI-PPW0W(NP\NP)/NP
narcotics, Wnarcotics,WNNWI-NPW0WN theWtheWDTWB-NPW0W (NP\NP)/N colonelWcolonelWNNWI-NPW0WN\N
said.Wsaid.WNNW I-NP W0WN
Figure 5 The output of C&C for the sentence "Drug dealers have lost some 3,600 kilos of narcotics, the colonel said".
4. Sentilo: Identifying Holders and
Topics of an Opinion Sentence
From an implementation perspective, our approach (named
Sentilo) is realized as a pipeline of components depicted in Figure 4, where colored components indicate novel contributions.
Components in dashed boxes are currently being developed. To
the best of our knowledge, Sentilo is the first method for opinion mining that hybridizes formal semantics, NLP and typical
computational intelligence techniques.
In order to explain the pipeline, let us consider the following sentence from the corpus we have used for the evaluation
(presented in Section 5): Drug dealers have lost some 3,600 kilos of
narcotics, the colonel said.
The syntactic constructs (cf. Section 3) are provided by
C&C [16], a combinatory categorial parser that integrates
named entity recognition, a categorial equivalent of dependency relation extraction, and co-reference resolution. The output of C&C for the sample sentence is depicted in Figure 5.
The output of C&C is processed by Boxer [4], an opensource software that produces event-based, verb-centric, semantic representations of natural language complying with Discourse Representation Theory (DRT) semantics. DRT is a
February 2014 | Ieee ComputatIonal IntellIgenCe magazIne
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Table of Contents for the Digital Edition of Computational Intelligence - February 2014
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