## 5 Limitations and Future Research Prospects  
Sentiment analysis research has made significant progress in the field but there are still challenges that limit its accuracy and applicability. These limitations include the difficulties in handling poorly structured and sarcastic texts, the lack of fine-grained sentiment analysis, the dependence on annotated data, the limitations of word embeddings, and the potential for bias in the training data. * Poorly Structured and Sarcastic Texts: Many sentiment analysis methods rely on structured and grammatically correct text, which can lead to inaccuracies in analyzing informal and poorly structured texts, such as social media posts, slang, and sarcastic comments. This is because the sentiments expressed in these types of texts can be subtle and require contextual understanding beyond surface-level analysis. * Coarse-Grained Sentiment Analysis: Although positive, negative, and neutral classes are commonly used in sentiment analysis, they may not capture the full range of emotions and intensities that a person can express. Fine-grained sentiment analysis, which categorizes emotions into more specific categories such as happy, sad, angry, or surprised, can provide more nuanced insights into the sentiment expressed in a text. * Lack of Cultural Awareness: Sentiment analysis models trained on data from a specific language or culture may not accurately capture the sentiments expressed in texts from other languages or cultures. This is because the use of language, idioms, and expressions can vary widely across cultures, and a sentiment analysis model trained on one culture may not be effective in analyzing sentiment in another culture. * Dependence on Annotated Data: Sentiment analysis algorithms often rely on annotated data, where humans manually label the sentiment of a text. However, collecting \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Classes** & \
\begin{tabular}{c} **Strongly** \\ **Positive** \\ \end{tabular} & **Positive** & **Neutral** & **Negative** &
\begin{tabular}{c} **Strongly** \\ **Negative** \\ \end{tabular} & **Total** \\ \hline IMDb & 2 & - & 25,000 & - & 25,000 & - & 50,000 \\ Twitter US Airline Sentiment & 3 & - & 2363 & 3099 & 9178 & - & 14,160 \\ Sentiment140 & 2 & - & 800,000 & - & 800,000 & - & 1,600,000 \\ SemEval-2017 4A & 3 & - & 22,277 & 28,528 & 11,812 & - & 62,617 \\ SemEval-2017 4B & 2 & - & 17,414 & - & 7735 & - & 25,149 \\ SemEval-2017 4C & 5 & 1151 & 15,254 & 19,187 & 6943 & 476 & 43,011 \\ SemEval-2017 4D & 2 & - & 17,414 & - & 7735 & - & 25,149 \\ SemEval-2017 4E & 5 & 1151 & 15,254 & 19,187 & 6943 & 476 & 43,011 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of sentiment analysis datasets. and labeling a large dataset can be time-consuming and resource-intensive, which can limit the scope of analysis to a specific domain or language. * Shortcomings of Word Embeddings: Word embeddings, which are a popular technique used in deep learning-based sentiment analysis, can be limited in capturing the complex relationships between words and their meanings in a text. This can result in a model that does not accurately represent the sentiment expressed in a text, leading to inaccuracies in analysis. * Bias in Training Data: The training data used to train a sentiment analysis model can be biased, which can impact the model's accuracy and generalization to new data. For example, a dataset that is predominantly composed of texts from one gender or race can lead to a model that is biased toward that group, resulting in inaccurate predictions for texts from other groups. To address these challenges, future research can focus on improving the accuracy and scope of sentiment analysis.