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Sentiment analysis JSON example (#193)
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* Add example for adding context information for prompting

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* add self ask for prompting

* Add sentiment analysis JSON example

* Update colab link

* Update colab link for self ask

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shilpakancharla authored Jun 5, 2024
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "UvTPRErEO2cl"
},
"source": [
"##### Copyright 2024 Google LLC."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "LCJL7_hQO3jW"
},
"outputs": [],
"source": [
"# @title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"# you may not use this file except in compliance with the License.\n",
"# You may obtain a copy of the License at\n",
"#\n",
"# https://www.apache.org/licenses/LICENSE-2.0\n",
"#\n",
"# Unless required by applicable law or agreed to in writing, software\n",
"# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"# See the License for the specific language governing permissions and\n",
"# limitations under the License."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "sP8PQnz1QrcF"
},
"source": [
"# Gemini API: Sentiment Analysis"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "bxGr_x3MRA0z"
},
"source": [
"<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://colab.research.google.com/github/google-gemini/cookbook/blob/main/examples/json_capabilities/Sentiment_Analysis.ipynb\"><img src = \"https://www.tensorflow.org/images/colab_logo_32px.png\"/>Run in Google Colab</a>\n",
" </td>\n",
"</table>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ysy--KfNRrCq"
},
"source": [
"You will use the Gemini to extract sentiment scores of reviews."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"id": "Ne-3gnXqR0hI"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/158.8 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[91m━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m10.2/158.8 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m \u001b[32m112.6/158.8 kB\u001b[0m \u001b[31m1.9 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m158.8/158.8 kB\u001b[0m \u001b[31m1.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h"
]
}
],
"source": [
"!pip install -U -q google-generativeai"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"id": "EconMHePQHGw"
},
"outputs": [],
"source": [
"import google.generativeai as genai\n",
"\n",
"import json\n",
"from typing_extensions import TypedDict # in python 3.12 replace typing_extensions with typing"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "eomJzCa6lb90"
},
"source": [
"## Configure your API key\n",
"\n",
"To run the following cell, your API key must be stored it in a Colab Secret named `GOOGLE_API_KEY`. If you don't already have an API key, or you're not sure how to create a Colab Secret, see [Authentication](https://github.com/google-gemini/cookbook/blob/main/quickstarts/Authentication.ipynb) for an example."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"id": "v-JZzORUpVR2"
},
"outputs": [],
"source": [
"from google.colab import userdata\n",
"GOOGLE_API_KEY=userdata.get('GOOGLE_API_KEY')\n",
"\n",
"genai.configure(api_key=GOOGLE_API_KEY)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "R3EUoLgJNfe7"
},
"source": [
"## Example"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "cblGFW3VwzyO"
},
"source": [
"Start by defining how you want your JSON to be returned and which categories you would like to classify an item by. After that,gGo ahead and define some examples. In this case, you are trying to classify reviews as positive, neutral, or negative."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"id": "QGdJnd0AOKbu"
},
"outputs": [],
"source": [
"sentiment_llm = genai.GenerativeModel(model_name='gemini-1.5-flash-latest',\n",
" generation_config={\"response_mime_type\": \"application/json\"})\n",
"prompt = \"\"\"\n",
"Generate each sentiment score probability (positive, negative, or neutral) for the whole text.\n",
"\n",
"Use the following schema to return the sentiment scores:\n",
"\n",
"class Sentiment(TypedDict):\n",
" positive_sentiment_score: float\n",
" negative_sentiment_score: float\n",
" neutral_sentiment_score: float\n",
"\n",
"{review}\"\"\""
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"id": "yRq9PGpVQLUK"
},
"outputs": [],
"source": [
"negative_review = \"This establishment is an insult to the culinary arts, with inedible food that left me questioning the chef's sanity and the health inspector's judgment.\"\n",
"positive_review = \"This restaurant is a true gem with impeccable service and a menu that tantalizes the taste buds. Every dish is a culinary masterpiece, crafted with fresh ingredients and bursting with flavor.\"\n",
"neutral_review = \"The restaurant offers a decent dining experience with average food and service, making it a passable choice for a casual meal.\""
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "aanwkFCtxKCi"
},
"source": [
"Here is a helper function to print formatted JSON:"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"id": "xyqeFZ-9QPV4"
},
"outputs": [],
"source": [
"def print_json(response):\n",
" print(json.dumps(json.loads(response.text), indent=4))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "z0yHqEJRwp_U"
},
"source": [
"Take a look at each of the probabilities returned to see how each of these reviews would be classified by the Gemini model."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"id": "Tz0cDFyD9uUT"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{\n",
" \"positive_sentiment_score\": 0.0,\n",
" \"negative_sentiment_score\": 0.9999999403953552,\n",
" \"neutral_sentiment_score\": 5.96046448e-08\n",
"}\n"
]
}
],
"source": [
"response = sentiment_llm.generate_content(prompt.format(review=negative_review))\n",
"print_json(response)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"id": "bMW3QmYy9uUT"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{\n",
" \"positive_sentiment_score\": 0.987756,\n",
" \"negative_sentiment_score\": 0.012244,\n",
" \"neutral_sentiment_score\": 0.0\n",
"}\n"
]
}
],
"source": [
"response = sentiment_llm.generate_content(prompt.format(review=positive_review))\n",
"print_json(response)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"id": "QTjUYa4J9uUT"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{\n",
" \"positive_sentiment_score\": 0.213,\n",
" \"negative_sentiment_score\": 0.193,\n",
" \"neutral_sentiment_score\": 0.594\n",
"}\n"
]
}
],
"source": [
"response = sentiment_llm.generate_content(prompt.format(review=neutral_review))\n",
"print_json(response)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "AGOx4_7r1uN6"
},
"source": [
"## Summary\n",
"You have now used Gemini to analyze the sentiment of restaurant reviews with JSON, but you can try it out on other types of texts, such as comments under a video or emails.\n",
"\n",
"Please see the other notebooks in this directory to learn more about how you can use the Gemini API for other JSON related tasks."
]
}
],
"metadata": {
"colab": {
"name": "Sentiment_Analysis.ipynb",
"toc_visible": true
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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