Question: What are the flaws in the system?
Evidence: It is important to understand how the model performs in different scenarios. Table TABREF20 analyzes when the system performs the best and worst for each emotion. Since the features used are mostly lexicon based, the system has difficulties in capturing the overall sentiment and it leads to amplifying or vanishing intensity signals. For instance, in example 4 of fear louder and shaking lexicons imply fear but overall sentence doesn't imply fear. A similar pattern can be found in the 4 example of Anger and 3 example of Joy. The system has difficulties in understanding of sarcastic tweets, for instance, in the 3 tweet of Anger the user expressed anger but used lol which is used in a positive sense most of the times and hence the system did a bad job at predicting intensity. The system also fails in predicting sentences having deeper emotion and sentiment which humans can understand with a little context. For example, in sample 4 of sadness, the tweet refers to post travel blues which humans can understand. But with little context, it is difficult for the system to accurately estimate the intensity. The performance is poor with very short sentences as there are fewer indicators to provide a reasonable estimate.
It is important to understand how the model performs in different scenarios. Table TABREF20 analyzes when the system performs the best and worst for each emotion. Since the features used are mostly lexicon based, the system has difficulties in capturing the overall sentiment and it leads to amplifying or vanishing intensity signals. For instance, in example 4 of fear louder and shaking lexicons imply fear but overall sentence doesn't imply fear. A similar pattern can be found in the 4 example of Anger and 3 example of Joy. The system has difficulties in understanding of sarcastic tweets, for instance, in the 3 tweet of Anger the user expressed anger but used lol which is used in a positive sense most of the times and hence the system did a bad job at predicting intensity. The system also fails in predicting sentences having deeper emotion and sentiment which humans can understand with a little context. For example, in sample 4 of sadness, the tweet refers to post travel blues which humans can understand. But with little context, it is difficult for the system to accurately estimate the intensity. The performance is poor with very short sentences as there are fewer indicators to provide a reasonable estimate.
It is important to understand how the model performs in different scenarios. Table TABREF20 analyzes when the system performs the best and worst for each emotion. Since the features used are mostly lexicon based, the system has difficulties in capturing the overall sentiment and it leads to amplifying or vanishing intensity signals. For instance, in example 4 of fear louder and shaking lexicons imply fear but overall sentence doesn't imply fear. A similar pattern can be found in the 4 example of Anger and 3 example of Joy. The system has difficulties in understanding of sarcastic tweets, for instance, in the 3 tweet of Anger the user expressed anger but used lol which is used in a positive sense most of the times and hence the system did a bad job at predicting intensity. The system also fails in predicting sentences having deeper emotion and sentiment which humans can understand with a little context. For example, in sample 4 of sadness, the tweet refers to post travel blues which humans can understand. But with little context, it is difficult for the system to accurately estimate the intensity. The performance is poor with very short sentences as there are fewer indicators to provide a reasonable estimate.
Since the features used are mostly lexicon based, the system has difficulties in capturing the overall sentiment and it leads to amplifying or vanishing intensity signals. For instance, in example 4 of fear louder and shaking lexicons imply fear but overall sentence doesn’t imply fear. A similar pattern can be found in the 4th example of Anger and 3rd example of Joy. The system has difficulties in understanding of sarcastic tweets, for instance, in the 3rd tweet of Anger the user expressed anger but used lol which is used in a positive sense most of the times and hence the system did a bad job at predicting intensity. The system also fails in predicting sentences having deeper emotion and sentiment which humans can understand with a little context.
Answer: It is difficult for the system to capture the overall sentiment and it leads to amplifying or vanishing intensity signals. The system also fails in predicting sentences having deeper emotion and sentiment which humans can understand with a little context.