Task 5:

Aspect Based Sentiment Analysis (ABSA)

We focus on slots 1 and 3 of subtask 1 first

Subtask 1: Sentence-level ABSA

Given a review text about a target entity (laptop, restaurant, etc.),

identify the following information:

• Slot 1: Aspect Category
  • ​ex. ''It is extremely portable and easily connects to WIFI at the library and elsewhere''
    ----->{LAPTOP#PORTABILITY}, {LAPTOP#CONNECTIVITY}
• Slot 2: Opinion Target Expression (OTE)
  • ​an expression used in the given text to refer to the reviewed E#A
  • ​ex. ''The fajitas were delicious, but expensive''
    ----->{FOOD#QUALITY, “fajitas”}, {FOOD#PRICES, “fajitas”}
• Slot 3: Sentiment Polarity
  • ​label: (positive, negative, or neutral) 

Slot1 : Aspect Detection

Subtask1: Sentence-level

Slot1 : Aspect Detection

bag of word : 62 %

glove vector :  61 %

bag of word + glove vector : 64 %

Restaurant

bag of word :  48 %

glove vector :  42%

bag of word + glove vector :  47 %

Laptop

Linear SVM

Slot1 : Aspect Detection

bag of word :  %

glove vector :  61 %

bag of word + glove vector :  64 %

Restaurant

bag of word :   %

glove vector : %

bag of word + glove vector :   %

Laptop

RBF SVM

Experiment in Slot1 (Cross Validation)

Without LSI :

• With Tf-Idf indexing

restaurant : 61% (12 category)

laptop : 52% (81 category)

• Without Tf-Idf indexing

restaurant : 60% (12 category)

laptop : 50.05% (81 category)

Experiment in Slot1 (Cross Validation)

With LSI : (reduce dimension to 1000)

restaurant : 60% (12 category)

laptop : 52% (81 category)

Need more time...

Experiment in Slot1 (Cross Validation)

Bigram 

Trigram 

Experiment in Slot1 (Visualization of LSI)

Experiment in Slot1 (Visualization of LSI)

Experiment in Slot1 (Visualization of LSI)

Experiment in Slot1 (unsupervised LDA)

Experiment in Slot1 (supervised LDA)

Experiment in Slot1 (Summary)

Two kinds of classifier

• discriminative model : 

• generative model : 

use sample to classify directly

use sample to guess hidden model's param <learn a model>
use these models to predict unseen sample <classify>

Slot3 : Polarity Detection

Subtask1: Sentence-level

Slot3 : 

Polarity Detection

• Small Experiment: predict by training a TreeLSTM model

Slot3 : Polarity Identification

Proposed Model

Slot3 : 

Polarity Detection

• Predict by training the proposed model

• Details:
  • Word embedding: GloVe vectors
  • Target: 5 classes
  • structure: dependency tree
  • learning rate: 0.005; embedding learning rate: 0.1
  • dropout: 0.5; activation: tanh
  • ​​​restaurant: 2 layers, 300 hidden
  • laptop: 3 layers, 300 hidden

• sentence Features obtain from last layer of TreeLSTM

• Stage 1: TreeLSTM

• when training TreeLSTM, conflicting labels for different aspects in a sentence are removed

• To extract sentence features for next stage

Slot3 : 

Polarity Detection

• Details:
  • Target: 3 classes
  • learning rate: 0.0001
  • dropout: 0.3
  • ​​​3 layers, 256 hidden
  • activation: relu

• Experimented with NN, will try SVM in the future

• Stage 2: Classifier

• conflicting labels can be trained in this stage

Slot3 : 

Polarity Detection

Results

Future Work

Before submission deadline (1/31)

Slot 1: Aspect Detection

Subtask 1: Sentence-level

Slot 2: OTE detection

Structured Learning

Subtask 1: Sentence-level

Slot 3: Polarity Detection

1. SVM in stage 2
2. Label internal nodes when training stage 1 TreeLSTM
3. Use other resources
4. Write our own TreeLSTM so our model can be trained end-to-end (if time permits...)
5. QA memory network (if time permits...)

Subtask 1: Sentence-level

sentence

Subtask1-slot1 SVM

See if sentence contains aspect

if yes

predict polarity by Subtask1-slot3 model

For each sentence:

Finally, combine all aspect and polarity pairs

Subtask 2: Text-level

Subtask 3: Out-of-domain