Adjustment to new changes

Home.py

@@ -32,8 +32,7 @@ with mt1:
    st.text('We support Scopus, Web of Science, Lens, as well as personalized CSV files. Further information can be found in the "How to" section.')
    st.text('')
    st.divider()
-   st.write('To cite the Coconut Library Tool, please use the following reference:') 
-   st.info(" Santosa, Faizhal Arif, George, Crissandra J., & Lamba, Manika. (2023). Coconut Library Tool (1.0.0). Zenodo. https://doi.org/10.5281/zenodo.8323458", icon="✍️")
+   st.info("We moved to https://www.coconut-libtool.com/", icon="🚨")
          
 with mt2:
    st.header("Before you start")
@@ -95,27 +94,26 @@ with mt2:
             st.write('Lamba, M., & Madhusudhan, M. (2021, July 31). Topic Modeling. Text Mining for Information Professionals, 105–137. https://doi.org/10.1007/978-3-030-85085-2_4')
 
         with tab2:
-            st.text("1. Put your file. We use abstract column for this process.")
+            st.text("1. Put your file. Choose your preferred column.")
             st.text("2. Choose your preferred method. LDA is the most widely used, whereas Biterm is appropriate for short text, and BERTopic works well for large text data as well as supports more than 50+ languages.")
             st.text("3. Finally, you can visualize your data.")
             st.error("This app includes lemmatization and stopwords for the abstract text. Currently, we only offer English words.", icon="💬")
-            st.error("If you want to see the topic on another data (chats, questionnaire, or other text), change the column name of your table to 'Abstract'.", icon="🚨")
             
         with tab3:
             st.text("""
             +----------------+------------------------+----------------------------------+
             |     Source     |       File Type        |              Column              |
             +----------------+------------------------+----------------------------------+
-            | Scopus         | Comma-separated values | Abstract                         |
-            |                | (.csv)                 |                                  |
-            +----------------+------------------------+----------------------------------+
-            | Web of Science | Tab delimited file     | Abstract                         |
+            | Scopus         | Comma-separated values | Choose your preferred column     |
+            |                | (.csv)                 | that you have                    |
+            +----------------+------------------------|                                  |
+            | Web of Science | Tab delimited file     |                                  |
             |                | (.txt)                 |                                  |
-            +----------------+------------------------+----------------------------------+
-            | Lens.org       | Comma-separated values | Abstract (Scholarly Works)       |
+            +----------------+------------------------|                                  |
+            | Lens.org       | Comma-separated values |                                  |
             |                | (.csv)                 |                                  |
-            +----------------+------------------------+----------------------------------+
-            | Other          | .csv                   | Change your column to 'Abstract' |
+            +----------------+------------------------|                                  |
+            | Other          | .csv                   |                                  |
             +----------------+------------------------+----------------------------------+
             """)
 
@@ -140,7 +138,7 @@ with mt2:
             st.write("The use of network text analysis by librarians can be quite beneficial. Finding hidden correlations and connections in textual material is a significant advantage. Using network text analysis tools, librarians can improve knowledge discovery, obtain deeper insights, and support scholars meaningfully, ultimately enhancing the library's services and resources. This menu provides a two-way relationship instead of the general network of relationships to enhance the co-word analysis. Since it is based on ARM, you may obtain transactional data information using this menu. Please name the column in your file 'Keyword' instead.")
             st.divider()
             st.write('💡 The idea came from this:') 
-            st.write('Santosa, F. A. (2023). Adding Perspective to the Bibliometric Mapping Using Bidirected Graph. Open Information Science, 7(1), 20220152. https://doi.org/10.1515/opis-2022-0152')
+            st.write('Santosa, F. (2023). Adding Perspective to the Bibliometric Mapping Using Bidirected Graph. Open Information Science, 7(1), 20220152. https://doi.org/10.1515/opis-2022-0152')
 
         with tab2:
             st.text("1. Put your file.")