108 lines
3.6 KiB
Python
108 lines
3.6 KiB
Python
import pandas as pd
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from ollama import Client
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import json
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import matplotlib.pyplot as plt
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import random
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import matplotlib.colors as mcolors
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def blue_color_func( word, font_size, position, orientation, random_state=None, **kwargs):
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# Use the provided random_state for reproducibility if available, else use random module
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r = random_state if random_state else random
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# Sample from the darker end of the 'Blues' colormap (e.g., 0.4 to 1.0)
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# 0.0 is white/light, 1.0 is dark blue
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min_val, max_val = 0.4, 1.0
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color_val = r.uniform(min_val, max_val)
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# Get color from matplotlib colormap
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rgba = plt.cm.Blues(color_val)
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return mcolors.to_hex(rgba)
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def worker_extraction(row, host, model):
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# Instantiate local client for this specific worker/thread
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local_client = Client(host=host)
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return ollama_keyword_extraction(
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content=row['content'],
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tag=row['tag'],
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client=local_client,
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model=model
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)
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def ollama_keyword_extraction(content, tag, client: Client, model) -> list:
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"""
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Perform sentiment analysis using Ollama model.
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Parameters:
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- content: Text content to analyze
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- tag: Tag indicating the type of sentiment analysis (e.g., 'VT - Positive')
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Returns:
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- sentiment score and reason
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"""
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# Construct prompt for Ollama model
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prompt = f"""
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### Role
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You are a qualitative data analyst. Your task is to extract keywords from a user quote to build a semantic word cluster.
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### Guidelines
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1. **Quantity:** Extract **1-5** high-value keywords. If the quote only contains 1 valid insight, return only 1 keyword. Do not force extra words.
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2. **Specificity:** Avoid vague, single nouns (e.g., "tech", "choice", "system"). Instead, capture the descriptor (e.g., "tech-forward", "payment choice", "legacy system").
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3. **Adjectives:** Standalone adjectives are acceptable if they are strong descriptors (e.g., "reliable", "trustworthy", "professional").
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4. **Normalize:** Convert verbs to present tense and nouns to singular.
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5. **Output Format:** Return a single JSON object with the key "keywords" containing a list of strings.
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### Examples
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**Input Context:** Chase as a Brand
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**Input Quote:** "I would describe it as, you know, like the next big thing, like, you know, tech forward, you know, customer service forward, and just hating that availability."
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**Output:** {{ "keywords": ["tech forward", "customer service focused", "availability"] }}
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**Input Context:** App Usability
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**Input Quote:** "There are so many options when I try to pay, it's confusing."
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**Output:** {{ "keywords": ["confusing", "payment options"] }}
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**Input Context:** Investment Tools
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**Input Quote:** "It is just really reliable."
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**Output:** {{ "keywords": ["reliable"] }}
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### Input Data
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**Context/Theme:** {tag}
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**Quote:** "{content}"
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### Output
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```json
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"""
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max_retries = 3
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for attempt in range(max_retries):
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try:
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resp = client.generate(
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model=model,
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prompt=prompt,
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format='json',
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)
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response_text = resp.response.strip()
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# Extract JSON from response
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start_index = response_text.find('{')
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if start_index == -1:
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raise ValueError("No JSON found")
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response_json, _ = json.JSONDecoder().raw_decode(response_text[start_index:])
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keywords = response_json.get('keywords', [])
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return [keywords]
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except Exception as e:
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print(f"Attempt {attempt + 1}/{max_retries} failed: {e}. Output was: {response_text}")
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if attempt == max_retries - 1:
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return [[]] |