Add description for GatorMiner features

doc/FrequencyAnalysis.md

@@ -0,0 +1,3 @@
+Frequency analysis is the process of finding the frequency of letters, words, or groups of letters in a text.
+Frequency analysis can be useful to discover what words are being used a lot more then other words.
+Conclusions can be drawn from frequency analysis like a group's sentiment and themes within a text.

doc/Interactive.md

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+This section provide an interactive platform for user, as user can enter a text, a paragraph, or an essay in the prompter and choose which analyzer they want to evaluate their writing. There are four provided analyzer:Show token (return the text in token), Show named entity (return object, entity stated in the text), Show sentiment (return the degree of negativity or positivity of the text), and Show summary (return one short sentence summarized the entered text).

doc/SentimentAnalysis.md

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+Sentiment analysis is the process of using text analysis to identify and study states of emotion in regards to the input, which is subjective information.
+Sentiment analysis is able to identify the amount of sentiment each user has for what it is processing. It will give numbers based on each users sentiment.
+To each user, outputs of higher numbers mean a higher sentiment and lower numbers are given for lower sentiment. Then the numbers are graphed according to each user.

doc/Summary.md

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+What the summary analyzer is essentially what the name implies, it gives a sort of summary of all the assignments in the folder. How it functions is that it takes the first line for the prompt that it is given, such as what was the greatest challenge you encountered and it takes the responses and puts them into lines based on the individual's response. You can then see what the summary is from each person and how they responded to the prompt or if they responded to the prompt at all.

doc/TopicModelling.md

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+The topic modeling analyzer is an analyzing system that takes keywords and implements them into a graph that demonstrates the frequency they were used from each person. It doesn't take the number of times those keywords were used but rather if they were used from certain users. If one of the keywords was used it will be put into either a histogram graph or a scatter chart. You can also change the number of topics and the amount of words per topic. For instance topic 0 has the keywords design, java, and people while topic 3 has robot, race, and ethical. The topics are also separated by which assignment they are analyzing which give us a better feel of what it is that's being calculated, and where those keywords originate from.

streamlit_web.py

@@ -209,6 +209,14 @@ def df_preprocess(df):
 
 def frequency():
     """main function for frequency analysis"""
+
+    #Create button to return the description for frequency analyzer
+    freq_des = md.read_file('doc/FrequencyAnalysis.md')
+    if(st.button("Frequency Analysis Description", key = 1)):
+        st.write(freq_des)
+    else:
+        pass
+
     freq_type = st.sidebar.selectbox(
         "Type of frequency analysis", ["Overall", "Student", "Question"]
     )
@@ -356,6 +364,14 @@ def question_freq(freq_range):
 
 def sentiment():
     """main function for sentiment analysis"""
+
+    #Create button return description for sentiment analysis
+    sent_des = md.read_file('doc/SentimentAnalysis.md')
+    if(st.button("Sentiment Analysis Description", key = 2)):
+        st.write(sent_des)
+    else:
+        pass
+
     senti_df = main_df.copy(deep=True)
     # calculate overall sentiment from the combined text
     senti_df[cts.SENTI] = senti_df["combined"].apply(
@@ -436,6 +452,14 @@ def question_senti(input_df):
 
 def summary():
     """Display summarization"""
+
+    #Create button return description for summary feature
+    summ_des = md.read_file('doc/Summary.md')
+    if(st.button("Summary Description", key = 3)):
+        st.write(summ_des)
+    else:
+        pass
+
     sum_df = preprocessed_df[
         preprocessed_df[assign_id].isin(assignments)
     ].dropna(axis=1, how="all")
@@ -448,6 +472,13 @@ def summary():
 
 def tpmodel():
     """Display topic modeling"""
+    #Create button return description for summary feature
+    topic_des = md.read_file('doc/TopicModelling.md')
+    if(st.button("Topic Modelling Description", key = 4)):
+        st.write(topic_des)
+    else:
+        pass
+
     topic_df = main_df.copy(deep=True)
     topic_df = topic_df[topic_df[assign_id].isin(assignments)]
     # st.write(topic_df)
@@ -548,6 +579,13 @@ def scatter_tm(lda_model, corpus, overall_topic_df):
 
 def doc_sim():
     """Display document similarity"""
+    #Create button return description for document similarity analyzer
+    docs_des = md.read_file('doc/DocumentSimilarity.md')
+    if(st.button("Document Similarity Description", key = 5)):
+        st.write(docs_des)
+    else:
+        pass
+
     doc_df = main_df.copy(deep=True)
     doc_sim_type = st.sidebar.selectbox(
         "Type of similarity analysis", ["TF-IDF", "Spacy"]
@@ -620,6 +658,14 @@ def spacy_sim(doc_df):
 
 def interactive():
     """Page to allow nlp analysis from user input"""
+
+    #Create button return description for interactive feature
+    inter_des = md.read_file('doc/Interactive.md')
+    if(st.button("Interactive Description", key = 6)):
+        st.write(inter_des)
+    else:
+        pass
+
     input_text = st.text_area("Enter text", "Type here")
     token_cb = st.checkbox("Show tokens")
     ner_cb = st.checkbox("Show named entities")