# Elevating Academic Advising: Natural Language Processing of Student Reviews
## 3 Research Methodology
### Sentiment Analysis Approach  
We obtained sentiment features by utilizing the widely recognized VADER (Valence Aware Dictionary for sEntiment Reasoning) tool for sentiment analysis through the NLTK natural language processing toolkit [12]. VADER computes compound sentiment analysis scores for written text, determining the positivity or negativity of a sentence based on the language used and the context of the text. It relies on a lexicon of word scores, which were crafted by ten independent human raters. Each word in the lexicon was assigned a score, which could range from \(-4\) (indicating an extremely negative sentiment) to \(+4\) (representing a highly positive sentiment), with 0 representing neutrality. VADER utilizes this lexicon as a basis for determining sentiment scores in a given text. It computes these scores and subsequently standardizes the outcome to a range between \(-1\) (indicating a strongly negative sentiment) and \(+1\) (indicating a highly positive sentiment). Its algorithm assigns scores, considering punctuation, capitalization, and word modifiers, providing a comprehensive analysis of the sentiment in a given set of text.  
Apart from the intensity of sentiment words, VADER identifies properties and specific attributes within the text, particularly in brief comments, influencing the perceived sentiment score of the text. For instance, an exclamation mark increases the intensity of sentiment orientation. Furthermore, it recognizes various sentiment-infused emoticons as well as sentiment-laden initialisms and acronyms. VADER accommodates negation by reversing the rating's sign when it is applied to words listed within the VADER lexicon. This enables the algorithm to accurately evaluate phrases like \"not friendly\" assigning them a negative score to reflect the altered context surrounding the word.  
In conjunction with this tool, we initiated the sentiment analysis process by tokenizing the collected textual reviews through NLTK's sophisticated tokenization methods, thus segmenting the comments into individual words or tokens for further analysis. Stop words--common but noninformative terms such as \"the\", \"and\", and \"or\"--were systematically removed from this tokenized corpus, ensuring that only significant, content-rich words contributed to the sentiment analysis. VADER proceeded to evaluate the sentiment scores for the refined set of words. It considered not just the emotional intensity of individual words but also their context within each sentence. Punctuation, capitalization, and word modifiers were all taken into account in the computation of sentiment scores, providing a nuanced analysis that considered the intricacies of language. Moreover, VADER intelligently accounted for various linguistic nuances such as negation, accurately assessing sentiments, even when phrases might reverse the expected sentiment due to negating terms. This meticulous methodology allowed for a comprehensive understanding of sentiment nuances within the student feedback, thus enabling a profound evaluation of perceptions and attitudes toward academic advisors.  
Regarding the utilization of lexicon analysis in our study, it is essential to clarify that although lexicon analysis itself is not directly categorized as a machine learning approach, the lexicon used has its roots in AI methodologies. VADER, as a sentiment analysis tool, was developed using machine learning techniques and natural language processing to construct its sentiment lexicon, which our study employed for sentiment analysis.