Analisis teks

Prompt Engineering dengan OpenAI API

Fouad Trad

Machine Learning Engineer

Analisis teks

Menganalisis teks untuk mengekstrak informasi
- Klasifikasi teks
- Ekstraksi entitas
Perusahaan sebaiknya berkonsultasi hukum saat memakai data pelanggan

Gambar yang menunjukkan analisis teks mengekstrak informasi seperti sentimen, emosi, nada, entitas, dll. dari teks.

Klasifikasi teks

Menetapkan kategori untuk teks
Contoh: analisis sentimen

Gambar yang menunjukkan kelas analisis sentimen: positif, negatif, atau netral.

Kategori ditentukan

Tentukan kategori klasifikasi jika sudah diketahui
Sebutkan kebutuhan keluaran

text = "I bought your XYZ Smart Watch and wanted to share my positive experience. 
Impressed with its sleek design, comfort, and touchscreen usability."


prompt = f"""Classify the sentiment of the text delimited by triple backticks as 
positive, negative, or neutral. Give your answer as a single word:
           ```{text}```"""

print(get_response(prompt))

positive

Kategori tidak ditentukan

Model menggunakan pengetahuannya saat kategori tidak ditentukan

text = "I bought your XYZ Smart Watch and wanted to share my positive experience. 
Impressed with its sleek design, comfort, and touchscreen usability."

prompt = f"""Classify the sentiment of the text delimited by triple backticks. 
             Give your answer as a single word.
           ```{text}```"""
print(get_response(response))

positive.

Untuk beberapa masalah open-ended ini mungkin tidak bekerja baik

Beberapa kelas

Teks dapat masuk ke beberapa kelas
Tetapkan jumlah kelas maksimum jika belum diketahui

text = "I bought your XYZ Smart Watch and wanted to share my positive experience. 
Impressed with its sleek design, comfort, and touchscreen usability."

prompt = f"""Identify emotions used in this text. Don't use more than 3 emotions. 
             Format your answer as a list of words separated by commas:
           ```{text}```"""

print(get_response(prompt))

impressed, positive, comfortable

Ekstraksi entitas

Mengekstrak entitas spesifik dari teks
Contoh: nama, tempat, organisasi, tanggal

Ikon yang menunjukkan ekstraksi entitas mengekstrak entitas spesifik dari teks

Ekstraksi entitas: tentukan entitas

Tentukan entitas yang akan diekstrak
Sebutkan format keluaran

text = "The XYZ Mobile X200: a sleek 6.5-inch Super AMOLED smartphone with a 48MP 
triple-camera, octa-core processor, 5000mAh battery, 5G connectivity, and Android 
11 OS. Secure with fingerprint and facial recognition. 128GB storage, expandable up 
to 512GB."

prompt = f"""Identify the following entities from the text delimited by triple backticks:
             - Product Name



           ```{text}```"""
print(get_response(prompt))

Ekstraksi entitas: tentukan entitas

Tentukan entitas yang akan diekstrak
Sebutkan format keluaran

text = "The XYZ Mobile X200: a sleek 6.5-inch Super AMOLED smartphone with a 48MP 
triple-camera, octa-core processor, 5000mAh battery, 5G connectivity, and Android 
11 OS. Secure with fingerprint and facial recognition. 128GB storage, expandable up 
to 512GB."

prompt = f"""Identify the following entities from the text delimited by triple backticks:
             - Product Name
             - Display Size


           ```{text}```"""
print(get_response(prompt))

Ekstraksi entitas: tentukan entitas

Tentukan entitas yang akan diekstrak
Sebutkan format keluaran

text = "The XYZ Mobile X200: a sleek 6.5-inch Super AMOLED smartphone with a 48MP 
triple-camera, octa-core processor, 5000mAh battery, 5G connectivity, and Android 
11 OS. Secure with fingerprint and facial recognition. 128GB storage, expandable up 
to 512GB."

prompt = f"""Identify the following entities from the text delimited by triple backticks:
             - Product Name
             - Display Size
             - Camera Resolution

           ```{text}```"""
print(get_response(prompt))

Ekstraksi entitas: tentukan entitas

Tentukan entitas yang akan diekstrak
Sebutkan format keluaran

text = "The XYZ Mobile X200: a sleek 6.5-inch Super AMOLED smartphone with a 48MP 
triple-camera, octa-core processor, 5000mAh battery, 5G connectivity, and Android 
11 OS. Secure with fingerprint and facial recognition. 128GB storage, expandable up 
to 512GB."

prompt = f"""Identify the following entities from the text delimited by triple backticks:
             - Product Name
             - Display Size
             - Camera Resolution
             Format the answer as an unordered list.
           ```{text}```"""
print(get_response(prompt))

Ekstraksi entitas: tentukan entitas

Product Name: XYZ Mobile X200
Display Size: 6.5-inch
Camera Resolution: 48MP triple-camera

Ekstraksi entitas dengan few-shot prompt

Untuk struktur yang kompleks

ticket_1 = "Hello, I'm Emma Adams. I'd 
like to ask about my reservation with 
the code CAR123. 
You can reach me at +123456 if needed."

ticket_2 = "This is Sarah Williams. 
I would like to request some information
regarding my upcoming flight with 
reservation code FLIGHT987. Thank you."

entities_1 = """
* Customer Details:
  - Name: Emma Adams
  - Phone: +123456
* Reservation Details:
  - Reservation Code: CAR123"""
entities_2 = """
* Customer Details:
  - Name: Sarah Williams
* Reservation Details:
  - Reservation Code: FLIGHT987"""

Ekstraksi entitas dengan few-shot prompt

ticket_3 = "Hello, I'm David Brown (CUST123). I need assistance with my reservation under 
the code HOTEL456. There are some questions and issues related to my upcoming stay that 
require your attention."


prompt = f"""Text: {ticket_1} -> Entities: {entities_1}
             Text: {ticket_2} -> Entities: {entities_2}
             Text: {ticket_3} -> Entities: """
print(get_response(prompt))

* Customer Details:
  - Name: David Brown
  - Customer ID: CUST123
* Reservation Details:
  - Reservation Code: HOTEL456

Ayo berlatih!

Prompt Engineering dengan OpenAI API