# Incorporating Word Significance into Aspect-Level Sentiment Analysis
## 4 Results And Discussion
### Analysis of Isan Model  
Table 3 illustrates the performance comparison of the ISAN model versions. The efficacy of incremental interpretation in aspect-level sentiment analysis is illustrated by the ISAN-I model, showing the importance of considering relevant aspect words that may be at the end of a sentence. This model is outperformed by the novelty decay based ISAN-D model, meaning that when these two factors are considered individually the impact of decaying novelty throughout a sentence is stronger. The influence of novelty decay implies that irrespective the length of a sentence, novelty is still an important factor as the stretched exponential law does not allow novelty to become zero even when the sentence length reaches large lengths. The performance of our model improves when novelty decay acts as a discounting factor of incremental interpretation as shown by the complete ISAN results, leading to 3.5%, 1.8% and 1% performance increase in the Laptop, Restaurant and Twitter datasets respectively. We also collected the statistics of the aspect word position in sentences from the Restaurant, Twitter and Laptop dataset as shown by Table 4. When each sentence length is divided into three segments, the majority of aspect words are located in the last section of the sentence for all datasets. This shows the importance of simultaneously considering words at the beginning and at the end of a sentence when determining sentiment polarity. The results also illustrate the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Laptop**} & \multicolumn{2}{c}{**Restaurant**} & \multicolumn{2}{c}{**Twitter**} \\ \cline{2-7}  & **Acc** & **Macro-F1** & **Acc** & **Macro-F1** & **Acc** & **Macro-F1** \\ \hline Majority & 0.535 & 0.333 & 0.650 & 0.333 & 0.500 & 0.333 \\ Feature-SVM & 0.705 & - & 0.802 & - & 0.634 & 0.633 \\ AE-LSTM & 0.689 & - & 0.762 & - & - & - \\ ATAE-LSTM & 0.687 & - & 0.772 & - & - & - \\ IAN & 0.721 & - & 0.786 & - & 0.716 * & 0.705 * \\ AOA-LSTM & 0.745 & - & 0.812 & - & 0.725 * & 0.706 * \\ ISAN & **0.749** & **0.721** & **0.824** & **0.744** & **0.734** & **0.716** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparison of different models on the three datasets. The baseline models results are accessed from published papers except for the ones marked with “*”. The values in bold represent the highest performance values.  
effectiveness of word significance in propagating the significance grading of a context word that may be far from an aspect word. Our results support our intuition that word significance can be used as a representation of how much a word contributes to the sentence interpretation and ultimately draws our model to words that are worthy of attention.  
This study demonstrates the relevance of word significance in aspect-level sentiment analysis and can be extended to other machine learning tasks based on the attention mechanism. Word significance may be used to incorporate attention filters which can guide the attention mechanism to items more worthy of attention, more especially in other NLP tasks where there is a possibility of time based novelty decay with repeated exposure. Depending on the field, the stretched exponential decay factor ranges between 0 and 1 as shown by Figure 3[48], putting emphasis on initial occurrences of an item without nullifying its later occurrences. Our results are also in line with previous studies which illustrate the universality of novelty decay in fields investigating social media and economics [21; 26; 42; 57], which may be further investigated using neural network based models. Ultimately, our model strives to closely represent human attention mechanisms in machine learning tasks.