Update method/Readme.md

method/Readme.md

@@ -109,7 +109,37 @@ We utilize Google BigQuery to extract Ethereum's blockchain data, including time
 
 #### Off-chain data
 Another dataset is users' discussion text from Discord. Discord hosts vibrant crypto discussions ranging from market analysis to technical debates, yet remains underexplored for sentiment analysis, unlike platforms like Twitter and Reddit, where extensive studies in cryptocurrency sentiment research exist \cite{kraaijeveld2020predictive,mohapatra2019kryptooracle,khan2022business}. We focus on critical communities such as Binance, Uniswap, and the Ethereum Dev channels on Discord. These are the communities for the largest centralized exchange, decentralized exchanges, and Ethereum developers, respectively. Analyzing sentiments from these communities, which influence Ethereum's network activity and gas usage through trading dynamics and developer engagement, provides crucial insights for predicting future gas demand and formulating effective network management strategies. The discussion texts are queries from Discord using the DiscordChatExporter, an open-source tool on GitHub.
-
+<table>
+        <thead>
+            <tr>
+                <th>AuthorID</th>
+                <th>Date</th>
+                <th>Content</th>
+                <th>Attach</th>
+            </tr>
+        </thead>
+        <tbody>
+            <tr>
+                <td>301186049323958272</td>
+                <td>2019-08-24 13:47</td>
+                <td>If you believe that each of these stable coins are eventually stable though, then you'll make money on the swinging back and forth.</td>
+                <td>NA</td>
+            </tr>
+            <tr>
+                <td>510252034310799360</td>
+                <td>2019-08-24 14:59</td>
+                <td>Heh, I was imagining a world where the pool has more liquidity than the rest of the market participants combined. Your, actually realistic, scenario makes it a little difficult for pool participants to exit.</td>
+                <td>NA</td>
+            </tr>
+            <tr>
+                <td>589621262733672448</td>
+                <td>2019-08-24 17:02</td>
+                <td>I kinda prefer variable price.</td>
+                <td>NA</td>
+            </tr>
+        </tbody>
+    </table>
+<caption>Off-chain chat examples</caption>
 #### Data processing 
 we also incorporate an additional off-chain data source, specifically the discussion text from Discord. To analyze this data, we use a large language model to process English sentences or words, estimating the probability of each sentence being classified as positive, negative, or neutral, ensuring that the total probability sums to 1. After obtaining sentiment information, we organize the corpus sequentially and compute average sentiment scores over both hourly and daily intervals. This sentiment information is denoted as gammma. We then synchronize the on-chain data with the off-chain sentiment using corresponding block data from the previous time chunk, ensuring that only preceding sentiment information is included in the training data.