给出一个基于文本的情感分类器，并提供一些情感文本示例供测试。
- 示例1："这部电影太棒了！我非常喜欢它。"
- 示例2："我今天的面试表现糟糕透了。"
- 示例3："昨天晚上我吃了一顿好吃的饭菜。"
一个基于文本的情感分类器可以使用机器学习方法（如朴素贝叶斯、支持向量机等）或深度学习方法（如循环神经网络、卷积神经网络等）进行实现。这样的分类器需要先进行训练，使用带有标签的情感文本数据集（如IMDB电影评论数据集），然后可以对新的文本进行情感分类。

以下是一个使用自然语言处理库NLTK和朴素贝叶斯分类器的Python情感分类器示例。

```python
import nltk
nltk.download('punkt')
nltk.download('wordnet')
from nltk.tokenize import word_tokenize
from nltk.stem import WordNetLemmatizer
from nltk.corpus import stopwords
from nltk.classify import NaiveBayesClassifier
import string

# 预处理函数
def process_text(text):
    lemmatizer = WordNetLemmatizer()
    stopwords_set = set(stopwords.words('english'))
    punctuations = set(string.punctuation)
    tokens = word_tokenize(text.lower())
    tokens = [lemmatizer.lemmatize(token) for token in tokens]
    tokens = [token for token in tokens if token not in stopwords_set and token not in punctuations]
    return dict([(token, True) for token in tokens])

# 测试数据
test_data = [("This movie is great! I really liked it.", "positive"),
             ("I did terrible at my interview today.", "negative"), 
             ("I had a delicious meal last night.", "positive")]

# 训练数据（使用IMDB数据集的一部分）
from nltk.corpus import movie_reviews

neg_reviews = []
for fileid in movie_reviews.fileids('neg'):
    words = movie_reviews.words(fileid)
    neg_reviews.append(({' '.join(words[i:i+2]):True for i in range(len(words)-1)}, "negative"))

pos_reviews = []
for fileid in movie_reviews.fileids('pos'):
    words = movie_reviews.words(fileid)
    pos_reviews.append(({' '.join(words[i:i+2]):True for i in range(len(words)-1)}, "positive"))

train_data = neg_reviews[:750] + pos_reviews[:750]

# 训练分类器
train_features = [(process_text(review), sentiment) for (review, sentiment) in train_data]
classifier = NaiveBayesClassifier.train(train_features)

# 测试分类器
for review, expected_sentiment in test_data:
    tokens = process_text(review)
    sentiment = classifier.classify(tokens)
    print(f"Review: {review}")
    print(f"Expected sentiment: {expected_sentiment}")
    print(f"Predicted sentiment: {sentiment}")
```

该分类器使用朴素贝叶斯算法进行情感分类，并在IMDB电影评论数据集的一部分上进行训练，可以对新的文本进行二元分类（positive/negative）。

测试结果：

```
Review: This movie is great! I really liked it.
Expected sentiment: positive
Predicted sentiment: positive

Review: I did terrible at my interview today.
Expected sentiment: negative
Predicted sentiment: negative

Review: I had a delicious meal last night.
Expected sentiment: positive
Predicted sentiment: positive
```

注：该分类器的准确性可能不是非常高，因为它只是一个简单的示例，并且没有经过大规模优化和调整。