# Sentiment-Aware Word Embedding for Emotion Classification
## 4 Experiments
### Relation to Other Method  
Multi-entity sentiment analysis using entity-level feature extraction and word embeddings approach [39] enhance the word embeddings approach with the deployment of a sentiment lexicon-based technique. The paper proposes associating a given entity with the adjectives, adverbs, and verbs describing it and extracting the associated sentiment to try and infer if the text is positive or negative in relation to the entity or entities. We discuss the major differences between [PERSON]'s model and our method: (i) Lexicon is used in different ways, [PERSON]'s model uses the lexicon against the parsed text to identify the polarity of the descriptor words (ii) [PERSON]'s model uses a Twitter-specific parser to identify the descriptor words that relate to a specific entity for text that contains multiple entities. The descriptor words of the multi-entity tweets are scored using SentiWordNet sentiment lexicon. The overall scoring per entity is printed out as output for the multi-entity tweets. The remaining tweets are classified using a random forest classifier. At the end of the article, the author points out the research aims to highlight how a hybrid word embeddings and lexicon-based approach can be used to tackle the problem of sentiment analysis on multiple entities. But we can see that it isolates dictionary information from word embedding techniques and does not use so-called hybrid word embedding techniques. And our approach is the perfect way to achieve this.  
We have reproduced the experiment of the paper and compared it with our method:  
As is shown in the Table 6, sentiment-aware word embedding is superior to [PERSON]'s model in the use of external information, and performs better on emotion classification than [PERSON]'s model. To sum up, the hybrid word vector that combines semantic information with external emotional information not only provides more word feature information but also involves the emotion labeling of the word in the model prediction. The experiments showed that this method is effective, and it is superior to the original model in terms of accuracy, recall rate, and F-score. It can be concluded that in the emotional analysis task, the quality of the word vector can be improved by incorporating external emotional information.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Prec** & **Rec** & **F1** \\ \hline SentiWordNet + POS & 0.66 & 0.66 & 0.66 \\ Word2 Vec + SentiWordNet + POS & 0.6913 & 0.6754 & 0.695 \\ Hybrid word vector1 & 0.7211 & 0.729 & 0.7099 \\ Hybrid word vector2 & 0.6871 & 0.7027 & 0.6865 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Accuracy comparison of [PERSON]’s model and sentiment-aware word embedding.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Word Vector** & **Prec** & **Rec** & **F1** \\ \hline \multirow{3}{*}{svm} & Skip-gram & 0.6685 & 0.6811 & 0.6542 \\  & Hybrid word vector1 & 0.7211 & 0.7290 & 0.7099 \\  & Hybrid word vector2 & 0.6871 & 0.7027 & 0.6865 \\ \hline \multirow{3}{*}{LR} & Skip-gram & 0.6612 & 0.6507 & 0.6674 \\  & Hybrid word vector1 & 0.7124 & 0.7015 & 0.6969 \\  & Hybrid word vector2 & 0.6932 & 0.7166 & 0.6925 \\ \hline \multirow{3}{*}{decision\_tree} & Skip-gram & 0.6366 & 0.6100 & 0.6230 \\  & Hybrid word vector1 & 0.6453 & 0.6170 & 0.6312 \\  & Hybrid word vector2 & 0.6379 & 0.6149 & 0.6143 \\ \hline \multirow{3}{*}{gradient\_boost} & Skip-gram & 0.6812 & 0.7511 & 0.7145 \\  & Hybrid word vector1 & 0.7102 & 0.7981 & 0.7475 \\ \cline{1-1}  & Hybrid word vector2 & 0.6968 & 0.7645 & 0.7251 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Accuracy comparison of emotion classification experiments based on various text representations.